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Relevant bibliographies by topics / Network Similarity Score (NSS)

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Academic literature on the topic 'Network Similarity Score (NSS)'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Network Similarity Score (NSS)"

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Lever, James, Robert Brkljača, Colin Rix, and Sylvia Urban. "Application of Networking Approaches to Assess the Chemical Diversity, Biogeography, and Pharmaceutical Potential of Verongiida Natural Products." Marine Drugs 19, no. 10 (2021): 582. http://dx.doi.org/10.3390/md19100582.

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This study provides a review of all isolated natural products (NPs) reported for sponges within the order Verongiida (1960 to May 2020) and includes a comprehensive compilation of their geographic and physico-chemical parameters. Physico-chemical parameters were used in this study to infer pharmacokinetic properties as well as the potential pharmaceutical potential of NPs from this order of marine sponge. In addition, a network analysis for the NPs produced by the Verongiida sponges was applied to systematically explore the chemical space relationships between taxonomy, secondary metabolite an

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Igumnov, Sergey, Konstantin Zbarousky, Pavel Lapanau, Ruslan Popok, and Ekaterina Grinevich. "ALEXITHYMIA AS RISK FACTOR OF THE DEVELOPMENT OF ADDICTIVE BEHAVIOR OF YOUNG PEOPLE IN THE REPUBLIC OF BELARUS." Visuomenės sveikata 28, no. 4 (2018): 47–50. http://dx.doi.org/10.5200/sm-hs.2018.049.

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A socio-psychological survey and clinical-psychological research were conducted (level of alexithymia; intensity of internet addiction; individual-characterological personality traits; individual style of coping strategies, behavioral patterns and resources of personality) in the cohort of 150 people at the age of 15-24, average age: 18.9±1.64, M:F= 87:63). As a result of the randomization of subjects, 3 groups were detected: themain group (people with traces of developing anaddiction from “new psychoactive substances” (NPS)) (MG, 50 people); a comparison group (people with “non-chemical”

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Bharti, Puja, and Deepti Mittal. "An Ultrasound Image Enhancement Method Using Neutrosophic Similarity Score." Ultrasonic Imaging 42, no. 6 (2020): 271–83. http://dx.doi.org/10.1177/0161734620961005.

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Ultrasound images, having low contrast and noise, adversely impact in the detection of abnormalities. In view of this, an enhancement method is proposed in this work to reduce noise and improve contrast of ultrasound images. The proposed method is based on scaling with neutrosophic similarity score (NSS), where an image is represented in the neutrosophic domain through three membership subsets T, I, and F denoting the degree of truth, indeterminacy, and falseness, respectively. The NSS measures the belonging degree of pixel to the texture using multi-criteria that is based on intensity, local

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Cai, Xinlu, Yongming Wang, Hanyu Zhou, et al. "M158. ASSOCIATIONS OF NEUROLOGICAL SOFT SIGNS AND CEREBELLAR-CEREBRAL FUNCTIONAL CONNECTIVITY IN PATIENTS WITH FIRST-EPISODE SCHIZOPHRENIA AND THEIR UNAFFECTED SIBLINGS." Schizophrenia Bulletin 46, Supplement_1 (2020): S196. http://dx.doi.org/10.1093/schbul/sbaa030.470.

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Abstract Background Neurological softs signs (NSS) are defined as subtle neurological abnormalities with manifestations of motor coordination, sensory integration and disinhibition. Evidence has suggested NSS as one of the most promising endophenotypes for schizophrenia spectrum disorders. Moreover, accumulating evidence also suggest that NSS may be associated with specific functional connectivity. The present study aimed to examine the cerebellar-cerebral resting-state functional connectivity (rsFC) of NSS in patients with first-episode schizophrenia (FES) and their unaffected siblings (SB).

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Luo, Jiasai, Sen Zhou, Yiming Li, et al. "Polarization Orientation Method Based on Remote Sensing Image in Cloudy Weather." Remote Sensing 15, no. 5 (2023): 1225. http://dx.doi.org/10.3390/rs15051225.

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Autonomous navigation technology is a core technology for intelligent operation, allowing the vehicles to perform tasks without relying on external information, which effectively improves the concealability and reliability. In this paper, based on the previous research on the bionic compound eye, a multi-channel camera array with different polarization degrees was used to construct the atmospheric polarization state measurement platform. A polarization trough threshold segmentation algorithm was applied to study the distribution characteristics and characterization methods of polarization stat

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Tuli, Shikhar, Bhishma Dedhia, Shreshth Tuli, and Niraj K. Jha. "FlexiBERT: Are Current Transformer Architectures too Homogeneous and Rigid?" Journal of Artificial Intelligence Research 77 (May 6, 2023): 39–70. http://dx.doi.org/10.1613/jair.1.13942.

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The existence of a plethora of language models makes the problem of selecting the best one for a custom task challenging. Most state-of-the-art methods leverage transformer-based models (e.g., BERT) or their variants. However, training such models and exploring their hyperparameter space is computationally expensive. Prior work proposes several neural architecture search (NAS) methods that employ performance predictors (e.g., surrogate models) to address this issue; however, such works limit analysis to homogeneous models that use fixed dimensionality throughout the network. This leads to sub-

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Ding, Jiujie, Jiahuan Zhang, Zongqian Zhan, Xiaofang Tang, and Xin Wang. "A Precision Efficient Method for Collapsed Building Detection in Post-Earthquake UAV Images Based on the Improved NMS Algorithm and Faster R-CNN." Remote Sensing 14, no. 3 (2022): 663. http://dx.doi.org/10.3390/rs14030663.

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The results of collapsed building detection act as an important reference for damage assessment after an earthquake, which is crucial for governments in order to efficiently determine the affected area and execute emergency rescue. For this task, unmanned aerial vehicle (UAV) images are often used as the data sources due to the advantages of high flexibility regarding data acquisition time and flying requirements and high resolution. However, collapsed buildings are typically distributed in both connected and independent pieces and with arbitrary shapes, and these are generally more obvious in

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Galphat, Yugchhaya. "TD score: Time Aware Domain Similarity based Link Prediction." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 3621–27. http://dx.doi.org/10.22214/ijraset.2022.43176.

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Abstract: Online Social Network has gained immense traction of users in past decade. Link prediction across social networks has become a new exploration area for researchers, where existing links are investigated and new links are anticipated among billions of online customers. Majority of work in this area focusses on exploring the current status of a particular network at a specific time, without exploring the behavior of the network links as time goes by. Only a Small amount of work has been performed with the consideration of temporal aspect of network. As the interests and interactions of

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Ghosh, Sambit, Vasundhara Gadiyaram, and Saraswathi Vishveshwara. "Validation of protein structure models using network similarity score." Proteins: Structure, Function, and Bioinformatics 85, no. 9 (2017): 1759–76. http://dx.doi.org/10.1002/prot.25332.

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Gupta, Anand Kumar, and Neetu Sardana. "Performance Analysis of Naïve Bayes Classifier Over Similarity Score-Based Techniques for Missing Link Prediction in Ego Networks." Journal of Information Technology Research 14, no. 1 (2021): 110–22. http://dx.doi.org/10.4018/jitr.2021010107.

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Dissertations / Theses on the topic "Network Similarity Score (NSS)"

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Mebratu, Ashagrie Kefyalew. "Does religious similarity influence the direction of trade? : Evidence from US bilateral trade with other 168 countries." Thesis, Södertörns högskola, Institutionen för samhällsvetenskaper, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-17478.

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Despite interest in the influence of religion on economic activity by early economists like Adam Smith, modern economists have done little research on the subject. In light of the apparent religious fervour in many parts of the global economy, economists' seeming lack of interest in studying how religious cultures enhance or retard the globalization of economic activity is especially surprising. In general, trade theories have given less weight towards the reason for trade explanation on demand side. As a contrary to H-O theory Linder had proposed a theoretically sound and empirically consiste

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Gadiyaram, Vasundhara. "Graph Spectral Methods for Analysis of Protein Structures." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4280.

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Network representation of protein structures is an information-rich mode of examining protein structure, dynamics and its interactions with biomolecules. Graph spectral methods are extremely useful and powerful in analysing complex networks. This thesis is concerned with development of graph spectral methods for analysing networks and applying them to protein structure analysis. Some of the key problems of network science that are addressed here are network similarity assessment and identification of key components in networks. A new network similarity score (NSS) has been developed and has sh

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Dighe, Anasuya. "Studies on Dynamic Plasticity of Ligand Binding Sites in Proteins." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4236.

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Molecular recognition between proteins and their associated ligands constitutes ligand-induced protein rewiring thereby enabling the formation of a stable protein-ligand complex. The studies presented in this thesis address the conformational plasticity inherent to proteins by virtue of which they adapt to diverse ligands and orchestrate complex biological processes like signal transduction, transcription and protein-protein interaction. Adopting network theory based formalisms for understanding protein-ligand associations involve deconstructing the three-dimensional structure of a protein in

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Book chapters on the topic "Network Similarity Score (NSS)"

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Xie, Bo, and Long Chen. "Automatic Scoring Model of Subjective Questions Based Text Similarity Fusion Model." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_60.

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AbstractAI In this era, scene based translation and intelligent word segmentation are not new technologies. However, there is still no good solution for long and complex Chinese semantic analysis. The subjective question scoring still relies on the teacher's manual marking. However, there are a large number of examinations, and the manual marking work is huge. At present, the labor cost is getting higher and higher, the traditional manual marking method can't meet the demand The demand for automatic marking is increasingly strong in modern society. At present, the automatic marking technology of objective questions has been very mature and widely used. However, by reasons of the complexity and the difficulty of natural language processing technology in Chinese text, there are still many shortcomings in subjective questions marking, such as not considering the impact of semantics, word order and other issues on scoring accuracy. The automatic scoring technology of subjective questions is a complex technology, involving pattern recognition, machine learning, natural language processing and other technologies. Good results have been seen in the calculation method-based deep learning and machine learning. The rapid development of NLP technology has brought a new breakthrough for subjective question scoring. We integrate two deep learning models based on the Siamese Network through bagging to ensure the accuracy of the results, the text similarity matching model based on the birth networks and the score point recognition model based on the named entity recognition method respectively. Combining with the framework of deep learning, we use the simulated manual scoring method to extract and match the score point sequence of students’ answers with standard answers. The score recognition model effectively improves the efficiency of model calculation and long text keyword matching. The loss value of the final training score recognition model is about 0.9, and the accuracy is 80.54%. The accuracy of the training text similarity matching model is 86.99%, and the fusion model is single. The scoring time is less than 0.8s, and the accuracy is 83.43%.

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Banu, Syeda Furruka, Md Mostafa Kamal Sarker, Mohamed Abdel-Nasser, Hatem A. Rashwan, and Domenec Puig. "WEU-Net: A Weight Excitation U-Net for Lung Nodule Segmentation." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210154.

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Lung cancer is a dangerous non-communicable disease attacking both women and men and every year it causes thousands of deaths worldwide. Accurate lung nodule segmentation in computed tomography (CT) images can help detect lung cancer early. Since there are different locations and indistinguishable shapes of lung nodules in CT images, the accuracy of the existing automated lung nodule segmentation methods still needs further enhancements. In an attempt towards overcoming the above-mentioned challenges, this paper presents WEU-Net; an end-to-end encoder-decoder deep learning approach to accurately segment lung nodules in CT images. Specifically, we use a U-Net network as a baseline and propose a weight excitation (WE) mechanism to encourage the deep learning network to learn lung nodule-relevant contextual features during the training stage. WEU-Net was trained and validated on a publicly available CT images dataset called LIDC-IDRI. The experimental results demonstrated that WEU-Net achieved a Dice score of 82.83% and a Jaccard similarity coefficient of 70.55%.

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Ung, Hieu Trung, Huy Quang Ung, Trung T. Nguyen, and Binh T. Nguyen. "An Efficient Insect Pest Classification Using Multiple Convolutional Neural Network Based Models." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220287.

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Accurate insect pest recognition is significant to protect the crop or take the early treatment on the infected yield, and it helps reduce the loss for the agriculture economy. Designing an automatic pest recognition system is necessary as manual recognition is slow, time-consuming, and expensive. The Image-based pest classifier using the traditional computer vision method is not efficient due to the complexity. Insect pest classification is difficult because of various kinds, scales, shapes, complex backgrounds in the field, and high appearance similarity among insect species. With the rapid development of deep learning technology, the CNN-based method is the best way to develop a fast and accurate insect pest classifier. We present different convolutional neural network-based models for solving challenges in the insect pest recognition problem, including attention, feature pyramid, and fine-grained models. We evaluate our methods on two public datasets: the large-scale insect pest dataset, the IP102 benchmark dataset, and a smaller dataset, namely D0 in terms of the macro-average precision (MPre), the macro-average recall (MRec), the macro-average F1- score (MF1), the accuracy (Acc), and the geometric mean (GM). The experimental results show that combining these convolutional neural network-based models can better perform than the state-of-the-art methods on these two datasets. For instance, the highest accuracy we obtained on IP102 and D0 is 72.91% and 99.89%, respectively, bypassing the corresponding state-of-the-art accuracy: 67.1% (IP102) and 98.8% (D0). We also publish our codes for contributing to the current research related to the insect pest classification problem.

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Oliveira, Rosinei, Ádamo L. Santana, João C. W. A. Costa, et al. "Recent Advances and Challenges in Wireless QoE-Aware Multimedia Streaming Systems." In Advances in Multimedia and Interactive Technologies. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-61350-144-3.ch011.

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It is expected that multimedia applications will be the most abundant application in the Internet and thousands of new wireless and mobile users will produce and share multimedia streaming content ubiquitously. In this multimedia-aware system, it is important to assure the end-to-end quality level support for video and voice applications in wireless systems. Traditional Quality of Service techniques assure the delivery of those services with packet differentiation assurance and indicate the impact of multimedia traffic only on the network performance; however, they do not reflect the user’s perception. Recent advances in multimedia are exploring new Quality of Experience approaches and including metrics and control schemes in wireless networking systems in order to increase the user´s satisfaction and optimize network resources. Operations based on Quality of Experience can be used as an indicator of how a networking environment meets the end-user’s needs and new assessment and packet control approaches are still important challenges. This chapter presents an overview of the most recent advances and challenges in assessment and traffic conditioner procedures for wireless multimedia streaming systems. In addition, an intelligent packet dropper mechanism for IEEE 802.11e systems is proposed and evaluated by using the Network Simulator 2, real video sequences and Evalvid tool. The benefit and the impact of the proposed solution is evaluated by using well-know objective and subjective Quality of Experience metrics, namely, Peak Signal-to-Noise Ratio, Video Quality Metric, Structural Similarity Index and Mean Option Score.

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Conference papers on the topic "Network Similarity Score (NSS)"

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Belkhirat, Ahmed, Abdelghani Bouras, and Abdelkader Belkhir. "A New Similarity Measure for the Anomaly Intrusion Detection." In 2009 Third International Conference on Network and System Security. IEEE, 2009. http://dx.doi.org/10.1109/nss.2009.20.

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Bonti, Alessio, Ming Li, and Wen Shi. "Improving P2P IPTV random peers search through user similarity." In 2011 5th International Conference on Network and System Security (NSS). IEEE, 2011. http://dx.doi.org/10.1109/icnss.2011.6059970.

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Jin, Di, Luzhi Wang, Yizhen Zheng, et al. "CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/292.

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Graph similarity learning refers to calculating the similarity score between two graphs, which is required in many realistic applications, such as visual tracking, graph classification, and collaborative filtering. As most of the existing graph neural networks yield effective graph representations of a single graph, little effort has been made for jointly learning two graph representations and calculating their similarity score. In addition, existing unsupervised graph similarity learning methods are mainly clustering-based, which ignores the valuable information embodied in graph pairs. To th

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Maji, Subhadeep, Rohan Kumar, Manish Bansal, Kalyani Roy, and Pawan Goyal. "Logic Constrained Pointer Networks for Interpretable Textual Similarity." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/333.

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Systematically discovering semantic relationships in text is an important and extensively studied area in Natural Language Processing, with various tasks such as entailment, semantic similarity, etc. Decomposability of sentence-level scores via subsequence alignments has been proposed as a way to make models more interpretable. We study the problem of aligning components of sentences leading to an interpretable model for semantic textual similarity. In this paper, we introduce a novel pointer network based model with a sentinel gating function to align constituent chunks, which are represented

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Guo, Yuchen, Guiguang Ding, Jungong Han, Sicheng Zhao, and Bin Wang. "Implicit Non-linear Similarity Scoring for Recognizing Unseen Classes." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/680.

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Recognizing unseen classes is an important task for real-world applications, due to: 1) it is common that some classes in reality have no labeled image exemplar for training; and 2) novel classes emerge rapidly. Recently, to address this task many zero-shot learning (ZSL) approaches have been proposed where explicit linear scores, like inner product score, are employed to measure the similarity between a class and an image. We argue that explicit linear scoring (ELS) seems too weak to capture complicated image-class correspondence. We propose a simple yet effective framework, called Implicit N

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Liew, Alison Shu Lien, and Khai Yin Lim. "Gesture Recognition-Malaysian Sign Language Recognition with Convolutional Neural Network." In International Conference on Digital Transformation and Applications (ICDXA 2020). Tunku Abdul Rahman University College, 2020. http://dx.doi.org/10.56453/icdxa.2020.1010.

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Sign language is a communication medium for the deaf and vocally impaired. However, this language is not practised in public due to the deaf community being a minority and it takes time to learn and skilled manpower to assist the deaf in public interaction. Thus, this study aims to produce a Malaysian Sign Language Recognition (MSLR) application to recognise MSL alphabets to help normal people communicate with the deaf. The proposed work involves a few stages that consist of background subtraction to detect the moving hand, skin segmentation based on skin tones using YCbCr (Luminance, Chromina

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Park, David Keetae, Seungjoo Yoo, Hyojin Bahng, Jaegul Choo, and Noseong Park. "MEGAN: Mixture of Experts of Generative Adversarial Networks for Multimodal Image Generation." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/122.

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Recently, generative adversarial networks (GANs) have shown promising performance in generating realistic images. However, they often struggle in learning complex underlying modalities in a given dataset, resulting in poor-quality generated images. To mitigate this problem, we present a novel approach called mixture of experts GAN (MEGAN), an ensemble approach of multiple generator networks. Each generator network in MEGAN specializes in generating images with a particular subset of modalities, e.g., an image class. Instead of incorporating a separate step of handcrafted clustering of multiple

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Gupta, Aman, and Yadul Raghav. "Deep Learning Roles based Approach to Link Prediction in Networks." In 9th International Conference on Natural Language Processing (NLP 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101416.

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The problem of predicting links has gained much attention in recent years due to its vast application in various domains such as sociology, network analysis, information science, etc. Many methods have been proposed for link prediction such as RA, AA, CCLP, etc. These methods required hand-crafted structural features to calculate the similarity scores between a pair of nodes in a network. Some methods use local structural information while others use global information of a graph. These methods do not tell which properties are better than others. With an in-depth analysis of these methods, we

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Jiang, Haiyun, Li Cui, Zhe Xu, et al. "Relation Extraction Using Supervision from Topic Knowledge of Relation Labels." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/698.

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Explicitly exploring the semantics of a relation is significant for high-accuracy relation extraction, which is, however, not fully studied in previous work. In this paper, we mine the topic knowledge of a relation to explicitly represent the semantics of this relation, and model relation extraction as a matching problem. That is, the matching score between a sentence and a candidate relation is predicted for an entity pair. To this end, we propose a deep matching network to precisely model the semantic similarity between a sentence-relation pair. Besides, the topic knowledge also allows us to

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Edwards, Kristen M., Vaishnavi L. Addala, and Faez Ahmed. "Design Form and Function Prediction From a Single Image." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-71853.

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Abstract Estimating the form and functional performance of a design in the early stages can be crucial for a designer for effective ideation Humans have an innate ability to guess the size, shape, and type of a design from a single view. The brain fills in the unknowns in a fraction of a second. However, humans may struggle with estimating the performance of designs in the early stages of the design process without making prototypes or doing back-of-the-envelope calculations. In contrast, machines need information about the full 3D model of a design to understand its structure. Machines can es

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Reports on the topic "Network Similarity Score (NSS)"

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Wilson, A. M., and M. C. Kelman. Assessing the relative threats from Canadian volcanoes. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328950.

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This report presents an analysis of the threat posed by active volcanoes in Canada and outlines directives to bring Canadian volcano monitoring and research into alignment with global best practices. We analyse 28 Canadian volcanoes in terms of their relative threat to people, aviation and infrastructure. The methodology we apply to assess volcanic threat was developed by the United States Geological Survey (USGS) as part of the 2005 National Volcano Early Warning System (NVEWS). Each volcano is scored on a number of hazard and exposure factors, producing an overall threat score. The overall t

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Wilson, A. M., and M. C. Kelman. Assessing the relative threats from Canadian volcanoes. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328950.

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This report presents an analysis of the threat posed by active volcanoes in Canada and outlines directives to bring Canadian volcano monitoring and research into alignment with global best practices. We analyse 28 Canadian volcanoes in terms of their relative threat to people, aviation and infrastructure. The methodology we apply to assess volcanic threat was developed by the United States Geological Survey (USGS) as part of the 2005 National Volcano Early Warning System (NVEWS). Each volcano is scored on a number of hazard and exposure factors, producing an overall threat score. The overall t

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Griffin, Andrew, Sean Griffin, Kristofer Lasko, et al. Evaluation of automated feature extraction algorithms using high-resolution satellite imagery across a rural-urban gradient in two unique cities in developing countries. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40182.

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Feature extraction algorithms are routinely leveraged to extract building footprints and road networks into vector format. When used in conjunction with high resolution remotely sensed imagery, machine learning enables the automation of such feature extraction workflows. However, many of the feature extraction algorithms currently available have not been thoroughly evaluated in a scientific manner within complex terrain such as the cities of developing countries. This report details the performance of three automated feature extraction (AFE) datasets: Ecopia, Tier 1, and Tier 2, at extracting

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