Journal articles: 'Multi-aspect networks'

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Zhang, Qiuyue, and Ran Lu. "A Multi-Attention Network for Aspect-Level Sentiment Analysis." Future Internet 11, no. 7 (July 16, 2019): 157. http://dx.doi.org/10.3390/fi11070157.

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Aspect-level sentiment analysis (ASA) aims at determining the sentiment polarity of specific aspect term with a given sentence. Recent advances in attention mechanisms suggest that attention models are useful in ASA tasks and can help identify focus words. Or combining attention mechanisms with neural networks are also common methods. However, according to the latest research, they often fail to extract text representations efficiently and to achieve interaction between aspect terms and contexts. In order to solve the complete task of ASA, this paper proposes a Multi-Attention Network (MAN) model which adopts several attention networks. This model not only preprocesses data by Bidirectional Encoder Representations from Transformers (BERT), but a number of measures have been taken. First, the MAN model utilizes the partial Transformer after transformation to obtain hidden sequence information. Second, because words in different location have different effects on aspect terms, we introduce location encoding to analyze the impact on distance from ASA tasks, then we obtain the influence of different words with aspect terms through the bidirectional attention network. From the experimental results of three datasets, we could find that the proposed model could achieve consistently superior results.

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Zhang, Qiuyue, Ran Lu, Qicai Wang, Zhenfang Zhu, and Peiyu Liu. "Interactive Multi-Head Attention Networks for Aspect-Level Sentiment Classification." IEEE Access 7 (2019): 160017–28. http://dx.doi.org/10.1109/access.2019.2951283.

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Брусков, А. А. "Failure propagation in interdependent multi-level networks." Informacionno-technologicheskij vestnik, no. 2(28) (June 17, 2021): 76–90. http://dx.doi.org/10.21499/2409-1650-2021-28-2-76-90.

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В этой работе разрабатываются новый подход и алгоритмические инструменты для моделирования и анализа живучести сетей с разнородными узлами, а также рассматривается их применение в космических сетях. Космические сети позволяют совместно использовать ресурсы космических аппаратов на орбите, такие как хранение, обработка и обмен данными. Каждый космический аппарат в сети может иметь различный состав и функциональность подсистем, что приводит к неоднородности узлов. Большинство традиционных анализов живучести сетей предполагают однородность узлов и в результате не подходят для анализа космических сетей. Эта работа предполагает, что гетерогенные сети могут быть смоделированы как взаимозависимые многоуровневые сети, что позволяет проводить анализ их живучести. Многоуровневый аспект фиксирует разбивку сети в соответствии с общими функциональными возможностями в различных узлах и позволяет создавать однородные подсети, в то время как аспект взаимозависимости ограничивает сеть для захвата физических характеристик каждого узла. In this paper, we develop a new approach and algorithmic tools for modeling and analyzing the survivability of networks with heterogeneous nodes, and also consider their application in space networks. Space networks allow the sharing of spacecraft resources in orbit, such as data storage, processing, and exchange. Each spacecraft in the network may have a different composition and functionality of subsystems, which leads to heterogeneity of nodes. Most traditional network survivability analyses assume node homogeneity and as a result are not suitable for space network analysis. This work suggests that heterogeneous networks can be modeled as interdependent multi-level networks, allowing analysis of their survivability. The multi-level aspect captures the network breakdown according to the common functionality in different nodes and allows for the creation of homogeneous subnets, while the interdependence aspect restricts the network to capture the physical characteristics of each node.

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Lan, Zhouxin, Qing He, and Liu Yang. "Dual-Channel Interactive Graph Convolutional Networks for Aspect-Level Sentiment Analysis." Mathematics 10, no. 18 (September 13, 2022): 3317. http://dx.doi.org/10.3390/math10183317.

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Aspect-level sentiment analysis aims to identify the sentiment polarity of one or more aspect terms in a sentence. At present, many researchers have applied dependency trees and graph neural networks (GNNs) to aspect-level sentiment analysis and achieved promising results. However, when a sentence contains multiple aspects, most methods model each aspect independently, ignoring the issue of sentiment connection between aspects. To address this problem, this paper proposes a dual-channel interactive graph convolutional network (DC-GCN) model for aspect-level sentiment analysis. The model considers both syntactic structure information and multi-aspect sentiment dependencies in sentences and employs graph convolutional networks (GCN) to learn its node information representation. Particularly, to better capture the representations of aspect and opinion words, we exploit the attention mechanism to interactively learn the syntactic information features and multi-aspect sentiment dependency features produced by the GCN. In addition, we construct the word embedding layer by the BERT pre-training model to better learn the contextual semantic information of sentences. The experimental results on the restaurant, laptop, and twitter datasets show that, compared with the state-of-the-art model, the accuracy is up to 1.86%, 2.50, 1.36%, and 0.38 and the Macro-F1 values are up to 1.93%, 0.61%, and 0.4%, respectively.

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Qiu, Heqian, Hongliang Li, Qingbo Wu, Fanman Meng, King Ngi Ngan, and Hengcan Shi. "A2RMNet: Adaptively Aspect Ratio Multi-Scale Network for Object Detection in Remote Sensing Images." Remote Sensing 11, no. 13 (July 4, 2019): 1594. http://dx.doi.org/10.3390/rs11131594.

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Object detection is a significant and challenging problem in the study area of remote sensing and image analysis. However, most existing methods are easy to miss or incorrectly locate objects due to the various sizes and aspect ratios of objects. In this paper, we propose a novel end-to-end Adaptively Aspect Ratio Multi-Scale Network (A 2 RMNet) to solve this problem. On the one hand, we design a multi-scale feature gate fusion network to adaptively integrate the multi-scale features of objects. This network is composed of gate fusion modules, refine blocks and region proposal networks. On the other hand, an aspect ratio attention network is leveraged to preserve the aspect ratios of objects, which alleviates the excessive shape distortions of objects caused by aspect ratio changes during training. Experiments show that the proposed A 2 RMNet significantly outperforms the previous state of the arts on the DOTA dataset, NWPU VHR-10 dataset, RSOD dataset and UCAS-AOD dataset by 5.73 % , 7.06 % , 3.27 % and 2.24 % , respectively.

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Robinson, M., M. R. Azimi-Sadjadi, and J. Salazar. "Multi-Aspect Target Discrimination Using Hidden Markov Models and Neural Networks." IEEE Transactions on Neural Networks 16, no. 2 (March 2005): 447–59. http://dx.doi.org/10.1109/tnn.2004.841805.

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Corchuelo, Rafael, José A. Pérez, and Antonio Ruiz-Cortés. "Aspect-oriented interaction in multi-organisational web-based systems." Computer Networks 41, no. 4 (March 2003): 385–406. http://dx.doi.org/10.1016/s1389-1286(02)00398-5.

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Xu, Nan, Wenji Mao, and Guandan Chen. "Multi-Interactive Memory Network for Aspect Based Multimodal Sentiment Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 371–78. http://dx.doi.org/10.1609/aaai.v33i01.3301371.

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As a fundamental task of sentiment analysis, aspect-level sentiment analysis aims to identify the sentiment polarity of a specific aspect in the context. Previous work on aspect-level sentiment analysis is text-based. With the prevalence of multimodal user-generated content (e.g. text and image) on the Internet, multimodal sentiment analysis has attracted increasing research attention in recent years. In the context of aspect-level sentiment analysis, multimodal data are often more important than text-only data, and have various correlations including impacts that aspect brings to text and image as well as the interactions associated with text and image. However, there has not been any related work carried out so far at the intersection of aspect-level and multimodal sentiment analysis. To fill this gap, we are among the first to put forward the new task, aspect based multimodal sentiment analysis, and propose a novel Multi-Interactive Memory Network (MIMN) model for this task. Our model includes two interactive memory networks to supervise the textual and visual information with the given aspect, and learns not only the interactive influences between cross-modality data but also the self influences in single-modality data. We provide a new publicly available multimodal aspect-level sentiment dataset to evaluate our model, and the experimental results demonstrate the effectiveness of our proposed model for this new task.

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Singh, Omkar, and Vinay Rishiwal. "QoS Aware Multi-hop Multi-path Routing Approach in Wireless Sensor Networks." International Journal of Sensors, Wireless Communications and Control 9, no. 1 (July 15, 2019): 43–52. http://dx.doi.org/10.2174/2210327908666180703143435.

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Background & Objective: Wireless Sensor Network (WSN) consist of huge number of tiny senor nodes. WSN collects environmental data and sends to the base station through multi-hop wireless communication. QoS is the salient aspect in wireless sensor networks that satisfies end-to-end QoS requirement on different parameters such as energy, network lifetime, packets delivery ratio and delay. Among them Energy consumption is the most important and challenging factor in WSN, since the senor nodes are made by battery reserved that tends towards life time of sensor networks. Methods: In this work an Improve-Energy Aware Multi-hop Multi-path Hierarchy (I-EAMMH) QoS based routing approach has been proposed and evaluated that reduces energy consumption and delivers data packets within time by selecting optimum cost path among discovered routes which extends network life time. Results and Conclusion: Simulation has been done in MATLAB on varying number of rounds 400- 2000 to checked the performance of proposed approach. I-EAMMH is compared with existing routing protocols namely EAMMH and LEACH and performs better in terms of end-to-end-delay, packet delivery ratio, as well as reduces the energy consumption 13%-19% and prolongs network lifetime 9%- 14%.

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Xu, Guangtao, Peiyu Liu, Zhenfang Zhu, Jie Liu, and Fuyong Xu. "Attention-Enhanced Graph Convolutional Networks for Aspect-Based Sentiment Classification with Multi-Head Attention." Applied Sciences 11, no. 8 (April 18, 2021): 3640. http://dx.doi.org/10.3390/app11083640.

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The purpose of aspect-based sentiment classification is to identify the sentiment polarity of each aspect in a sentence. Recently, due to the introduction of Graph Convolutional Networks (GCN), more and more studies have used sentence structure information to establish the connection between aspects and opinion words. However, the accuracy of these methods is limited by noise information and dependency tree parsing performance. To solve this problem, we proposed an attention-enhanced graph convolutional network (AEGCN) for aspect-based sentiment classification with multi-head attention (MHA). Our proposed method can better combine semantic and syntactic information by introducing MHA and GCN. We also added an attention mechanism to GCN to enhance its performance. In order to verify the effectiveness of our proposed method, we conducted a lot of experiments on five benchmark datasets. The experimental results show that our proposed method can make more reasonable use of semantic and syntactic information, and further improve the performance of GCN.

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Cheng, Li-Chen, Yen-Liang Chen, and Yuan-Yu Liao. "Aspect-based sentiment analysis with component focusing multi-head co-attention networks." Neurocomputing 489 (June 2022): 9–17. http://dx.doi.org/10.1016/j.neucom.2022.03.027.

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Zhang, Fan, Chen Hu, Qiang Yin, Wei Li, Heng-Chao Li, and Wen Hong. "Multi-Aspect-Aware Bidirectional LSTM Networks for Synthetic Aperture Radar Target Recognition." IEEE Access 5 (2017): 26880–91. http://dx.doi.org/10.1109/access.2017.2773363.

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Liu, Jie, Peiyu Liu, Zhenfang Zhu, Xiaowen Li, and Guangtao Xu. "Graph Convolutional Networks with Bidirectional Attention for Aspect-Based Sentiment Classification." Applied Sciences 11, no. 4 (February 8, 2021): 1528. http://dx.doi.org/10.3390/app11041528.

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Aspect-based sentiment classification aims at determining the corresponding sentiment of a particular aspect. Many sophisticated approaches, such as attention mechanisms and Graph Convolutional Networks, have been widely used to address this challenge. However, most of the previous methods have not well analyzed the role of words and long-distance dependencies, and the interaction between context and aspect terms is not well realized, which greatly limits the effectiveness of the model. In this paper, we propose an effective and novel method using attention mechanism and graph convolutional network (ATGCN). Firstly, we make full use of multi-head attention and point-wise convolution transformation to obtain the hidden state. Secondly, we introduce position coding in the model, and use Graph Convolutional Networks to obtain syntactic information and long-distance dependencies. Finally, the interaction between context and aspect terms is further realized by bidirectional attention. Experiments on three benchmarking collections indicate the effectiveness of ATGCN.

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Li, Siyuan, Zongxu Pan, and Yuxin Hu. "Multi-Aspect Convolutional-Transformer Network for SAR Automatic Target Recognition." Remote Sensing 14, no. 16 (August 12, 2022): 3924. http://dx.doi.org/10.3390/rs14163924.

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In recent years, synthetic aperture radar (SAR) automatic target recognition (ATR) has been widely used in both military and civilian fields. Due to the sensitivity of SAR images to the observation azimuth, the multi-aspect SAR image sequence contains more information for recognition than a single-aspect one. Nowadays, multi-aspect SAR target recognition methods mainly use recurrent neural networks (RNN), which rely on the order between images and thus suffer from information loss. At the same time, the training of the deep learning model also requires a lot of training data, but multi-aspect SAR images are expensive to obtain. Therefore, this paper proposes a multi-aspect SAR recognition method based on self-attention, which is used to find the correlation between the semantic information of images. Simultaneously, in order to improve the anti-noise ability of the proposed method and reduce the dependence on a large amount of data, the convolutional autoencoder (CAE) used to pretrain the feature extraction part of the method is designed. The experimental results using the MSTAR dataset show that the proposed multi-aspect SAR target recognition method is superior in various working conditions, performs well with few samples and also has a strong ability of anti-noise.

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Shirahatti, Abhinandan, Vijay Rajpurohit, and Sanjeev Sannakki. "Transformer based multi-head attention network for aspect-based sentiment classification." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 1 (April 1, 2022): 472. http://dx.doi.org/10.11591/ijeecs.v26.i1.pp472-481.

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Aspect-based <span>sentiment classification is vital in helping manufacturers identify the pros and cons of their products and features. In the latest days, there has been a tremendous surge of interest in aspect-based sentiment classification (ABSC). Since it predicts an aspect term sentiment polarity in a sentence rather than the whole sentence. Most of the existing methods have used recurrent neural networks and attention mechanisms which fail to capture global dependencies of the input sequence and it leads to some information loss and some of the existing methods used sequence models for this task, but training these models is a bit tedious. Here, we propose the multi-head attention transformation (MHAT) network the MHAT utilizes a transformer encoder in order to minimize training time for ABSC tasks. First, we used a pre-trained Global vectors for word representation (GloVe) for word and aspect term embeddings. Second, part-of-speech (POS) features are fused with MHAT to extract grammatical aspects of an input sentence. Whereas most of the existing methods have neglected this. Using the SemEval 2014 dataset, the proposed model consistently outperforms the state-of-the-art methods on aspect-based sentiment classification tasks.</span>

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Chiha, Rim, Mounir Ben Ayed, and Célia da Costa Pereira. "A New Neural Networks-Based Integrated Model for Aspect Extraction and Sentiment Classification." International Journal of Multimedia Data Engineering and Management 12, no. 4 (October 2021): 52–71. http://dx.doi.org/10.4018/ijmdem.2021100104.

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The aspect-based sentiment analysis (ABSA) task consists of two closely related subtasks: aspect extraction and sentiment classification. However, the majority of previous studies looked into each task separately, limiting their effectiveness. In contrast, the integration of aspect extraction and sentiment classification into a single model improves results. The main focus in this work is to manage these two tasks into a new collapsed model. The proposed model relies upon the bidirectional long short-term memory (Bi-LSTM) architecture. On the one hand, it combines a multi-channel convolution layer with an optimization method for handling the aspect extraction task. On the other hand, it includes an attention mechanism based on the residual block and aspect position information for predicting the appropriate opinion orientation of an aspect. The experimental results demonstrate that the model achieved the best performance.

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Tilwari, Valmik, Taewon Song, and Sangheon Pack. "An Improved Routing Approach for Enhancing QoS Performance for D2D Communication in B5G Networks." Electronics 11, no. 24 (December 10, 2022): 4118. http://dx.doi.org/10.3390/electronics11244118.

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Device-to-device (D2D) communication is one of the eminent promising technologies in Beyond Fifth Generation (B5G) wireless networks. It promises high data rates and ubiquitous coverage with low latency, energy, and spectral efficiency among peer-to-peer users. These advantages enable D2D communication to be fully realized in a multi-hop communication scenario. However, to ideally implement multi-hop D2D communication networks, the routing aspect should be thoroughly addressed since a multi-hop network can perform worse than a conventional mobile system if wrong routing decisions are made without proper mechanisms. Thus, routing in multi-hop networks needs to consider device mobility, battery, link quality, and fairness, which issues do not exist in orthodox cellular networking. Therefore, this paper proposed a mobility, battery, link quality, and contention window size-aware routing (MBLCR) approach to boost the overall network performance. In addition, a multicriteria decision-making (MCDM) method is applied to the relay devices for optimal path establishment, which provides weights according to the evaluated values of the devices. Extensive simulation results under various device speed scenarios show the advantages of the MBLCR compared to conventional algorithms in terms of throughput, packet delivery ratio, latency, and energy efficiency.

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Wu, Hanqian, Siliang Cheng, Zhike Wang, Shangbin Zhang, and Feng Yuan. "Multi-task learning based on question–answering style reviews for aspect category classification and aspect term extraction on GPU clusters." Cluster Computing 23, no. 3 (July 27, 2020): 1973–86. http://dx.doi.org/10.1007/s10586-020-03160-9.

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Abstract Cluster computing technologies are rapidly advancing and user-generated online reviews are booming in the current Internet and e-commerce environment. The latest question–answering (Q&A)-style reviews are novel, abundant and easily digestible product reviews that also contain massive valuable information for customers. In this paper, we mine valuable aspect information of products contained in these reviews on GPU clusters. To achieve this goal, we utilize two subtasks of aspect-based sentiment analysis: aspect term extraction (ATE) and aspect category classification (ACC). Most previous works focused on only one task or solved these two tasks separately, even though they are highly interrelated, and they do not make full use of abundant training resources. To address this problem, we propose a novel multi-task neural learning model to jointly handle these two tasks and explore the performance of our model on GPU clusters. We conducted extensive comparative experiments on an annotated corpus and found that our proposed model outperforms several baseline models in ATE and ACC tasks on GPU clusters, yielding significant strides in data mining for these types of reviews.

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Meng, Fandong, Jinchao Zhang, Yang Liu, and Jie Zhou. "Multi-Zone Unit for Recurrent Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5150–57. http://dx.doi.org/10.1609/aaai.v34i04.5958.

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Recurrent neural networks (RNNs) have been widely used to deal with sequence learning problems. The input-dependent transition function, which folds new observations into hidden states to sequentially construct fixed-length representations of arbitrary-length sequences, plays a critical role in RNNs. Based on single space composition, transition functions in existing RNNs often have difficulty in capturing complicated long-range dependencies. In this paper, we introduce a new Multi-zone Unit (MZU) for RNNs. The key idea is to design a transition function that is capable of modeling multiple space composition. The MZU consists of three components: zone generation, zone composition, and zone aggregation. Experimental results on multiple datasets of the character-level language modeling task and the aspect-based sentiment analysis task demonstrate the superiority of the MZU.

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Dao, Anh-Hien, and Cheng-Zen Yang. "Severity Prediction for Bug Reports Using Multi-Aspect Features: A Deep Learning Approach." Mathematics 9, no. 14 (July 13, 2021): 1644. http://dx.doi.org/10.3390/math9141644.

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The severity of software bug reports plays an important role in maintaining software quality. Many approaches have been proposed to predict the severity of bug reports using textual information. In this research, we propose a deep learning framework called MASP that uses convolutional neural networks (CNN) and the content-aspect, sentiment-aspect, quality-aspect, and reporter-aspect features of bug reports to improve prediction performance. We have performed experiments on datasets collected from Eclipse and Mozilla. The results show that the MASP model outperforms the state-of-the-art CNN model in terms of average Accuracy, Precision, Recall, F1-measure, and the Matthews Correlation Coefficient (MCC) by 1.83%, 0.46%, 3.23%, 1.72%, and 6.61%, respectively.

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Xu, Huan, Shuxian Liu, Wei Wang, and Le Deng. "RAG-TCGCN: Aspect Sentiment Analysis Based on Residual Attention Gating and Three-Channel Graph Convolutional Networks." Applied Sciences 12, no. 23 (November 26, 2022): 12108. http://dx.doi.org/10.3390/app122312108.

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Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that mainly judges the polarity of a given aspect word in a review. Current methods mainly use graph networks to do aspect-level sentiment classification tasks, most of which use syntactic or semantic graphs, and utilize attention mechanisms to interact and correlate aspect terms and contexts to obtain more useful feature representations. However, these methods may ignore some insignificant syntactic structures and some implicit information in some sentences. The attention mechanism then easily loses the original information, which eventually leads to inaccurate sentiment analysis. In order to solve this problem, this paper proposes a model based on residual attention gating and three-channel graph convolutional network (RAG-TCGCN). Firstly, the model uses a three-channel network composed of syntactic information, semantic information, and public information to simultaneously optimize and fuse through the multi-head attention mechanism to solve the problem of sentences without significant syntactic structure and with implicit information. Through the residual attention gating mechanism the problem of loss of original information is solved. Experimental verification shows that the accuracy and F1 value of the model are improved on the three public datasets.

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Zhang, Zhengxuan, Zhihao Ma, Shaohua Cai, Jiehai Chen, and Yun Xue. "Knowledge-Enhanced Dual-Channel GCN for Aspect-Based Sentiment Analysis." Mathematics 10, no. 22 (November 15, 2022): 4273. http://dx.doi.org/10.3390/math10224273.

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As a subtask of sentiment analysis, aspect-based sentiment analysis (ABSA) refers to identifying the sentiment polarity of the given aspect. The state-of-the-art ABSA models are developed by using the graph neural networks to deal with the semantics and the syntax of the sentence. These methods are challenged by two issues. For one thing, the semantic-based graph convolution networks fail to capture the relation between aspect and its opinion word. For another, minor attention is assigned to the aspect word within graph convolution, resulting in the introduction of contextual noise. In this work, we propose a knowledge-enhanced dual-channel graph convolutional network. On the task of ABSA, a semantic-based graph convolutional netwok (GCN) and a syntactic-based GCN are established. With respect to semantic learning, the sentence semantics are enhanced by using commonsense knowledge. The multi-head attention mechanism is taken to construct the semantic graph and filter the noise, which facilitates the information aggregation of the aspect and the opinion words. For syntactic information processing, the syntax dependency tree is pruned to remove the irrelevant words, based on which more attention weights are given to the aspect words. Experiments are carried out on four benchmark datasets to evaluate the working performance of the proposed model. Our model significantly outperforms the baseline models and verifies its effectiveness in ABSA tasks.

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Gutiérrez-Gaitán, Miguel, and Patrick Meumeu Yomsi. "Multiprocessor Scheduling meets the Industrial Wireless." U.Porto Journal of Engineering 5, no. 1 (March 26, 2019): 59–76. http://dx.doi.org/10.24840/2183-6493_005.001_0005.

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This survey covers schedulability analysis approaches that have been recently proposed for multi-hop and multi-channel wireless sensor and actuator networks in the industrial control process domain. It reviews results with a focus on WirelessHART-like networks. The paper address the mapping of multi-channel transmission scheduling to multiprocessor scheduling theory, and recognize it as the key aspect of the research direction covered by this survey. It also provides a taxonomy of the existing approaches concerning this direction, and discuss its main features and evolution. The survey identifies open issues, key research challenges, and future directions.

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Liu, Gang, Yongshu Li, Zheng Li, and Jiawei Guo. "Cartographic generalization of urban street networks based on gravitational field theory." International Journal of Modern Physics B 28, no. 20 (June 19, 2014): 1450133. http://dx.doi.org/10.1142/s0217979214501331.

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The automatic generalization of urban street networks is a constant and important aspect of geographical information science. Previous studies show that the dual graph for street–street relationships more accurately reflects the overall morphological properties and importance of streets than do other methods. In this study, we construct a dual graph to represent street–street relationship and propose an approach to generalize street networks based on gravitational field theory. We retain the global structural properties and topological connectivity of an original street network and borrow from gravitational field theory to define the gravitational force between nodes. The concept of multi-order neighbors is introduced and the gravitational force is taken as the measure of the importance contribution between nodes. The importance of a node is defined as the result of the interaction between a given node and its multi-order neighbors. Degree distribution is used to evaluate the level of maintaining the global structure and topological characteristics of a street network and to illustrate the efficiency of the suggested method. Experimental results indicate that the proposed approach can be used in generalizing street networks and retaining their density characteristics, connectivity and global structure.

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Yu, Jianfei, Jing Jiang, and Rui Xia. "Global Inference for Aspect and Opinion Terms Co-Extraction Based on Multi-Task Neural Networks." IEEE/ACM Transactions on Audio, Speech, and Language Processing 27, no. 1 (January 2019): 168–77. http://dx.doi.org/10.1109/taslp.2018.2875170.

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Sheikhahmadi, Adib, Farshid Veisi, Amir Sheikhahmadi, and Shahnaz Mohammadimajd. "A multi-attribute method for ranking influential nodes in complex networks." PLOS ONE 17, no. 11 (November 28, 2022): e0278129. http://dx.doi.org/10.1371/journal.pone.0278129.

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Calculating the importance of influential nodes and ranking them based on their diffusion power is one of the open issues and critical research fields in complex networks. It is essential to identify an attribute that can compute and rank the diffusion power of nodes with high accuracy, despite the plurality of nodes and many relationships between them. Most methods presented only use one structural attribute to capture the influence of individuals, which is not entirely accurate in most networks. The reason is that network structures are disparate, and these methods will be inefficient by altering the network. A possible solution is to use more than one attribute to examine the characteristics aspect and address the issue mentioned. Therefore, this study presents a method for identifying and ranking node’s ability to spread information. The purpose of this study is to present a multi-attribute decision making approach for determining diffusion power and classification of nodes, which uses several local and semi-local attributes. Local and semi-local attributes with linear time complexity are used, considering different aspects of the network nodes. Evaluations performed on datasets of real networks demonstrate that the proposed method performs satisfactorily in allocating distinct ranks to nodes; moreover, as the infection rate of nodes increases, the accuracy of the proposed method increases.

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Anand, Santosh, and Akarsha RR. "A Protocol for The Effective Utilization of Energy in Wireless Sensor Network." International Journal of Engineering & Technology 7, no. 3.3 (June 21, 2018): 93. http://dx.doi.org/10.14419/ijet.v7i3.3.14495.

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Energy utilization is an important aspect in any Wireless Sensor Network .The data transmission from various components connected over real-time networks consumes more energy in Wireless Sensor Network. Mainly the task of any network engineer lies in performing an energy efficient, so to reserve the nonrenewable energy supply to sensor nodes. The research convey out effective utilization of energy in wireless sensor networks. It is important to comprise long-term and low-cost monitoring in different WSN application. The network algorithms separated mainly in two parts, first to generate multiple paths and second to switch paths from generated list of paths .Which is implemented as multi-hop-communication so that the battery life of the sensor node may live for long term and low cost of monitoring, which achieve the high lifetime of WSN.

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Liou, Yi-Hwa, Alan J. Daly, Chris Brown, and Miguel del Fresno. "Foregrounding the role of relationships in reform." International Journal of Educational Management 29, no. 7 (September 14, 2015): 819–37. http://dx.doi.org/10.1108/ijem-05-2015-0063.

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Purpose – The role of relationships in the process of leadership and change is central, yet the social aspect of the work of reform is often background in favor of more technical approaches to improvement. Therefore, the purpose of this paper is to argue that social network theory and analysis provides a useful theory and set of tools to unpack the complex social work of leadership. Design/methodology/approach – In this paper the authors begin by reviewing social network theory in education to date. The authors identify strengths and gap areas and use findings and data from existing social network studies of educational leadership to highlight major concepts. Findings – Along with empirical examples, the paper proposes four important strands of social network analysis for future research in educational leadership: multiplex networks; multi-mode networks; longitudinal networks; and real time networks. Originality/value – This paper builds on recent scholarship using social network analysis in educational leadership and suggests that social network theory and methods provides unique and important analytic purchase in the study of educational leadership.

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Ma, Ji, Wei Ni, Jie Yin, Ren Ping Liu, Yuyu Yuan, and Binxing Fang. "Modeling Mobile Cellular Networks Based on Social Characteristics." International Journal of Computers Communications & Control 11, no. 4 (July 3, 2016): 480. http://dx.doi.org/10.15837/ijccc.2016.4.2054.

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Social characteristics have become an important aspect of cellular systems, particularly in next generation networks where cells are miniaturised and social effects can have considerable impacts on network operations. Traffic load demonstrates strong spatial and temporal fluctuations caused by users social activities. In this article, we introduce a new modelling method which integrates the social aspects of individual cells in modelling cellular networks. In the new method, entropy based social characteristics and time sequences of traffic fluctuations are defined as key measures, and jointly evaluated. Spectral clustering techniques can be extended and applied to categorise cells based on these key parameters. Based on the social characteristics respectively, we implement multi-dimensional clustering technologies, and categorize the base stations. Experimental studies are carried out to validate our proposed model, and the effectiveness of the model is confirmed through the consistency between measurements and model. In practice, our modelling method can be used for network planning and parameter dimensioning to facilitate cellular network design, deployments and operations.

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Peng, Qiang, Yan Jiang, Gan Liu, Fei Cui, Guo Bao He, and Wei Min Wu. "Channel Reservation Strategies for Multiple Secondary Users in Cognitive Radio Networks." Advanced Materials Research 989-994 (July 2014): 3889–92. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3889.

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In cognitive radio networks (CRNs), improving the performance of SUs is the main aspect of present study. In this paper, we focus on the optimal access strategy to provide QoS guarantees for multi-services. The contribution of this paper is as follows. Firstly, we formulate and solve the optimal spectrum access problem with different channel reservation scheme. Secondly, we take account of buffer for handoff secondary users and propose a handoff scheme for multi-services. In addition, the computation complexity of solving the optimal channel reservation problem is simplified by a binary search. The system is analyzed using a continuous-time Markov chain model. The numerical results confirm the validity of the proposed scheme.

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Shanmugapriya, G., Manasa Y, Ali Baig Mohammad, and Sk Hasane Ahammad. "Using Multi-Label Multi-Class Support Vector Machines with Semantic and Lexical Features for Aspect Category Detection." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 11 (November 30, 2022): 07–13. http://dx.doi.org/10.17762/ijritcc.v10i11.5773.

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In contrast to the aspects, aspect categories are often coarser and don't always appear as terms in sentences. Besides, the typical way to element the types associated with part is generally grainier concerning factors and doesn't exist within verdicts. The primary intent of the study is to investigate the efficacy of Lexicon, linguistic, vector-based, and features correlated to semantics within the aspect of the responsibility built with the finding of aspect category detection ACD). Semantic and emotional data are captured via vector-based features. Further, it examines vector-based feature superiority issues within the compression of features of text-based characteristics. Study purposes to the linguistic efficacy with the Lexicon, linguistic, and semantic features, also vector-based dependent to the system. Also, the information led with vector-based features that capture the semantic with sentimental analysis characteristics. With the experimental outcomes, the performance efficacy with the vector-based features outperformed text-based features. The methodologies associated with deep learning have generated features within the vector orientation relevant to the word-based structures. Therefore, the proposed method achieved effectiveness with the determined constraints by applying the metrics of precision, recall, and F1 scores. Correlating with the performance of ABSA's state-of-the-art techniques, the proposed research process gained superior outcomes.

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Yamamoto, Shuhei, and Tetsuji Satoh. "Two phase estimation method for multi-classifying real life tweets." International Journal of Web Information Systems 10, no. 4 (November 11, 2014): 378–93. http://dx.doi.org/10.1108/ijwis-04-2014-0013.

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Purpose – This paper aims to propose a multi-label method that estimates appropriate aspects against unknown tweets using the two-phase estimation method. Many Twitter users share daily events and opinions. Some beneficial comments are posted on such real-life aspects as eating, traffic, weather and so on. Such posts as “The train is not coming” are categorized in the Traffic aspect. Such tweets as “The train is delayed by heavy rain” are categorized in both the Traffic and Weather aspects. Design/methodology/approach – The proposed method consists of two phases. In the first, many topics are extracted from a sea of tweets using Latent Dirichlet Allocation (LDA). In the second, associations among many topics and fewer aspects are built using a small set of labeled tweets. The aspect scores for tweets were calculated using associations based on the extracted terms. Appropriate aspects are labeled for unknown tweets by averaging the aspect scores. Findings – Using a large amount of actual tweets, the sophisticated experimental evaluations demonstrate the high efficiency of the proposed multi-label classification method. It is confirmed that high F-measure aspects are strongly associated with topics that have high relevance. Low F-measure aspects are associated with topics that are connected to many other aspects. Originality/value – The proposed method features two-phase semi-supervised learning. Many topics are extracted using an unsupervised learning model called LDA. Associations among many topics and fewer aspects are built using labeled tweets.

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Wang, Hao, Defu Lian, Hanghang Tong, Qi Liu, Zhenya Huang, and Enhong Chen. "HyperSoRec: Exploiting Hyperbolic User and Item Representations with Multiple Aspects for Social-aware Recommendation." ACM Transactions on Information Systems 40, no. 2 (April 30, 2022): 1–28. http://dx.doi.org/10.1145/3463913.

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Social recommendation has achieved great success in many domains including e-commerce and location-based social networks. Existing methods usually explore the user-item interactions or user-user connections to predict users’ preference behaviors. However, they usually learn both user and item representations in Euclidean space, which has large limitations for exploring the latent hierarchical property in the data. In this article, we study a novel problem of hyperbolic social recommendation, where we aim to learn the compact but strong representations for both users and items. Meanwhile, this work also addresses two critical domain-issues, which are under-explored. First, users often make trade-offs with multiple underlying aspect factors to make decisions during their interactions with items. Second, users generally build connections with others in terms of different aspects, which produces different influences with aspects in social network. To this end, we propose a novel graph neural network (GNN) framework with multiple aspect learning, namely, HyperSoRec. Specifically, we first embed all users, items, and aspects into hyperbolic space with superior representations to ensure their hierarchical properties. Then, we adapt a GNN with novel multi-aspect message-passing-receiving mechanism to capture different influences among users. Next, to characterize the multi-aspect interactions of users on items, we propose an adaptive hyperbolic metric learning method by introducing learnable interactive relations among different aspects. Finally, we utilize the hyperbolic translational distance to measure the plausibility in each user-item pair for recommendation. Experimental results on two public datasets clearly demonstrate that our HyperSoRec not only achieves significant improvement for recommendation performance but also shows better representation ability in hyperbolic space with strong robustness and reliability.

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Крыжановский, Б. В., Н. Н. Смирнов, В. Ф. Никитин, Я. М. Карандашев, М. Ю. Мальсагов, and Е. В. Михальченко. "Neural Networks Applications to Combustion Process Simulation." Успехи кибернетики / Russian Journal of Cybernetics, no. 4(8) (November 30, 2021): 15–29. http://dx.doi.org/10.51790/2712-9942-2021-2-4-2.

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Моделирование горения является ключевым аспектом полномасштабного трехмерного моделирования современных и перспективных двигателей для авиационно-космических силовых установок. В данной работе изучается возможность решения задач химической кинетики с использованием искусственных нейронных сетей. С помощью классических численных методов были построены наборы обучающих данных. Выбирая среди различных архитектур многослойных нейронных сетей и настраивая их параметры, мы разработали достаточно простую модель, способную решить эту проблему. Полученная нейронная сеть работает в рекурсивном режиме и может предсказывать поведение химической многовидовой динамической системы за много шагов. Combustion process simulations are the key aspect enabling full-scale 3D simulations of advanced aerospace engines. This work studies solving chemical kinetics problems with artificial neural networks. The training datasets were generated by classical numerical methods. Choosing a multi-layer neural network architecture and fine-tuning its parameters, we developed a simple model that can solve the problem. The neural network obtained works is recursive, and by running many iterations it can predict the behavior of a chemical multimodal dynamic system.  

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Sun, Jimeng, Charalampos E. Tsourakakis, Evan Hoke, Christos Faloutsos, and Tina Eliassi-Rad. "Two heads better than one: pattern discovery in time-evolving multi-aspect data." Data Mining and Knowledge Discovery 17, no. 1 (July 10, 2008): 111–28. http://dx.doi.org/10.1007/s10618-008-0112-3.

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Ippolito, Andre, and Jorge Rady de Almeida Junior. "A multi-aspect approach to ontology matching based on Bayesian cluster ensembles." Journal of Intelligent Information Systems 55, no. 1 (November 23, 2019): 95–118. http://dx.doi.org/10.1007/s10844-019-00583-8.

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Rajan, Rajeev, and Sreejith Sivan. "Raga Recognition in Indian Carnatic Music Using Convolutional Neural Networks." WSEAS TRANSACTIONS ON ACOUSTICS AND MUSIC 9 (May 7, 2022): 5–10. http://dx.doi.org/10.37394/232019.2022.9.2.

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A vital aspect of Indian Classical music (ICM) is raga, which serves as a melodic framework for compositions and improvisations for both traditions of classical music. In this work, we propose a CNN-based sliding window analysis on mel-spectrogram and modgdgram for raga recognition in Carnatic music. The impor- tant contribution of the work is that the pro- posed method neither requires pitch extraction nor metadata for the estimation of raga. CNN learns the representation of raga from the pat- terns in the melspectrogram/ modgdgram dur- ing training through a sliding-window analysis. We train and test the network on sliced-mel- spectrogram/modgdgram of the original audio while the nal inference is performed on the au- dio as a whole. The performance is evaluated on 15 ragas from the CompMusic dataset. Multi- stream fusion has also been implemented to identify the potential of two feature representations. Multi-stream architecture shows promise in the proposed scheme for raga recognition.

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Wu, Xinyi, Zhenyao Wu, Jinglin Zhang, Lili Ju, and Song Wang. "SalSAC: A Video Saliency Prediction Model with Shuffled Attentions and Correlation-Based ConvLSTM." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 12410–17. http://dx.doi.org/10.1609/aaai.v34i07.6927.

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The performance of predicting human fixations in videos has been much enhanced with the help of development of the convolutional neural networks (CNN). In this paper, we propose a novel end-to-end neural network “SalSAC” for video saliency prediction, which uses the CNN-LSTM-Attention as the basic architecture and utilizes the information from both static and dynamic aspects. To better represent the static information of each frame, we first extract multi-level features of same size from different layers of the encoder CNN and calculate the corresponding multi-level attentions, then we randomly shuffle these attention maps among levels and multiply them to the extracted multi-level features respectively. Through this way, we leverage the attention consistency across different layers to improve the robustness of the network. On the dynamic aspect, we propose a correlation-based ConvLSTM to appropriately balance the influence of the current and preceding frames to the prediction. Experimental results on the DHF1K, Hollywood2 and UCF-sports datasets show that SalSAC outperforms many existing state-of-the-art methods.

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Boodaghidizaji, Miad, Monsurul Khan, and Arezoo M. Ardekani. "Multi-fidelity modeling to predict the rheological properties of a suspension of fibers using neural networks and Gaussian processes." Physics of Fluids 34, no. 5 (May 2022): 053101. http://dx.doi.org/10.1063/5.0087449.

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Unveiling the rheological properties of fiber suspensions is of paramount interest to many industrial applications. There are multiple factors, such as fiber aspect ratio and volume fraction, that play a significant role in altering the rheological behavior of suspensions. Three-dimensional (3D) numerical simulations of coupled differential equations of the suspension of fibers are computationally expensive and time-consuming. Machine learning algorithms can be trained on the available data and make predictions for the cases where no numerical data are available. However, some widely used machine learning surrogates, such as neural networks, require a relatively large training dataset to produce accurate predictions. Multi-fidelity models, which combine high-fidelity data from numerical simulations and less expensive lower fidelity data from resources such as simplified constitutive equations, can pave the way for more accurate predictions. Here, we focus on neural networks and the Gaussian processes with two levels of fidelity, i.e., high and low fidelity networks, to predict the steady-state rheological properties, and compare them to the single-fidelity network. High-fidelity data are obtained from direct numerical simulations based on an immersed boundary method to couple the fluid and solid motion. The low-fidelity data are produced by using constitutive equations. Multiple neural networks and the Gaussian process structures are used for the hyperparameter tuning purpose. Results indicate that with the best choice of hyperparameters, both the multi-fidelity Gaussian processes and neural networks are capable of making predictions with a high level of accuracy with neural networks demonstrating marginally better performance.

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Abbaschian, Babak Joze, Daniel Sierra-Sosa, and Adel Elmaghraby. "Deep Learning Techniques for Speech Emotion Recognition, from Databases to Models." Sensors 21, no. 4 (February 10, 2021): 1249. http://dx.doi.org/10.3390/s21041249.

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The advancements in neural networks and the on-demand need for accurate and near real-time Speech Emotion Recognition (SER) in human–computer interactions make it mandatory to compare available methods and databases in SER to achieve feasible solutions and a firmer understanding of this open-ended problem. The current study reviews deep learning approaches for SER with available datasets, followed by conventional machine learning techniques for speech emotion recognition. Ultimately, we present a multi-aspect comparison between practical neural network approaches in speech emotion recognition. The goal of this study is to provide a survey of the field of discrete speech emotion recognition.

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Zhang, Yixin, Lizhen Cui, Wei He, Xudong Lu, and Shipeng Wang. "Behavioral data assists decisions: exploring the mental representation of digital-self." International Journal of Crowd Science 5, no. 2 (July 26, 2021): 185–203. http://dx.doi.org/10.1108/ijcs-03-2021-0011.

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Purpose The behavioral decision-making of digital-self is one of the important research contents of the network of crowd intelligence. The factors and mechanisms that affect decision-making have attracted the attention of many researchers. Among the factors that influence decision-making, the mind of digital-self plays an important role. Exploring the influence mechanism of digital-selfs’ mind on decision-making is helpful to understand the behaviors of the crowd intelligence network and improve the transaction efficiency in the network of CrowdIntell. Design/methodology/approach In this paper, the authors use behavioral pattern perception layer, multi-aspect perception layer and memory network enhancement layer to adaptively explore the mind of a digital-self and generate the mental representation of a digital-self from three aspects including external behavior, multi-aspect factors of the mind and memory units. The authors use the mental representations to assist behavioral decision-making. Findings The evaluation in real-world open data sets shows that the proposed method can model the mind and verify the influence of the mind on the behavioral decisions, and its performance is better than the universal baseline methods for modeling user interest. Originality/value In general, the authors use the behaviors of the digital-self to mine and explore its mind, which is used to assist the digital-self to make decisions and promote the transaction in the network of CrowdIntell. This work is one of the early attempts, which uses neural networks to model the mental representation of digital-self.

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Tang, Zhenhua, Jiemei Yao, and Qian Zhang. "Multi-operator image retargeting in compressed domain by preserving aspect ratio of important contents." Multimedia Tools and Applications 81, no. 1 (October 5, 2021): 1501–22. http://dx.doi.org/10.1007/s11042-021-11376-z.

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Tang, Zhenhua, Jiemei Yao, and Qian Zhang. "Multi-operator image retargeting in compressed domain by preserving aspect ratio of important contents." Multimedia Tools and Applications 81, no. 1 (October 5, 2021): 1501–22. http://dx.doi.org/10.1007/s11042-021-11376-z.

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AlGhamdi, Najwa, Shaheen Khatoon, and Majed Alshamari. "Multi-Aspect Oriented Sentiment Classification: Prior Knowledge Topic Modelling and Ensemble Learning Classifier Approach." Applied Sciences 12, no. 8 (April 18, 2022): 4066. http://dx.doi.org/10.3390/app12084066.

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User-generated content on numerous sites is indicative of users’ sentiment towards many issues, from daily food intake to using new products. Amid the active usage of social networks and micro-blogs, notably during the COVID-19 pandemic, we may glean insights into any product or service through users’ feedback and opinions. Thus, it is often difficult and time consuming to go through all the reviews and analyse them in order to recognize the notion of the overall goodness or badness of the reviews before making any decision. To overcome this challenge, sentiment analysis has been used as an effective rapid way to automatically gauge consumers’ opinions. Large reviews will possibly encompass both positive and negative opinions on different features of a product/service in the same review. Therefore, this paper proposes an aspect-oriented sentiment classification using a combination of the prior knowledge topic model algorithm (SA-LDA), automatic labelling (SentiWordNet) and ensemble method (Stacking). The framework is evaluated using the dataset from different domains. The results have shown that the proposed SA-LDA outperformed the standard LDA. In addition, the suggested ensemble learning classifier has increased the accuracy of the classifier by more than ~3% when it is compared to baseline classification algorithms. The study concluded that the proposed approach is equally adaptable across multi-domain applications.

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Hu, Li, Shilian Wang, and Eryang Zhang. "Aspect-Aware Target Detection and Localization by Wireless Sensor Networks." Sensors 18, no. 9 (August 25, 2018): 2810. http://dx.doi.org/10.3390/s18092810.

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This paper considers the active detection of a stealth target with aspect dependent reflection (e.g., submarine, aircraft, etc.) using wireless sensor networks (WSNs). When the target is detected, its localization is also of interest. Due to stringent bandwidth and energy constraints, sensor observations are quantized into few-bit data individually and then transmitted to a fusion center (FC), where a generalized likelihood ratio test (GLRT) detector is employed to achieve target detection and maximum likelihood estimation of the target location simultaneously. In this context, we first develop a GLRT detector using one-bit quantized data which is shown to outperform the typical counting rule and the detection scheme based on the scan statistic. We further propose a GLRT detector based on adaptive multi-bit quantization, where the sensor observations are more precisely quantized, and the quantized data can be efficiently transmitted to the FC. The Cramer-Rao lower bound (CRLB) of the estimate of target location is also derived for the GLRT detector. The simulation results show that the proposed GLRT detector with adaptive 2-bit quantization achieves much better performance than the GLRT based on one-bit quantization, at the cost of only a minor increase in communication overhead.

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Niccolai, Alessandro, Francesco Grimaccia, Marco Mussetta, Alessandro Gandelli, and Riccardo Zich. "Social Network Optimization for WSN Routing: Analysis on Problem Codification Techniques." Mathematics 8, no. 4 (April 14, 2020): 583. http://dx.doi.org/10.3390/math8040583.

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The correct design of a Wireless Sensor Network (WSN) is a very important task because it can highly influence its installation and operational costs. An important aspect that should be addressed with WSN is the routing definition in multi-hop networks. This problem is faced with different methods in the literature, and here it is managed with a recently developed swarm intelligence algorithm called Social Network Optimization (SNO). In this paper, the routing definition in WSN is approached with two different problem codifications and solved with SNO and Particle Swarm Optimization. The first codification allows the optimization algorithm more degrees of freedom, resulting in a slower and in many cases sub-optimal solution. The second codification reduces the degrees of freedom, speeding significantly the optimization process and blocking in some cases the convergence toward the real best network configuration.

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Bolz-Johnson, Matthew, Louise Clément, William Gahl, Carmencita Padilla, Yukiko Nishumura, Rachel Yang, Lisa Sarfaty, Nicoline Hoogerbrugge, Gareth Baynam, and Thomas Kenny. "Enhancing the value of clinical networks for rare diseases." Rare Disease and Orphan Drugs Journal 2, no. 2 (2022): 9. http://dx.doi.org/10.20517/rdodj.2022.01.

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Healthcare networks for rare diseases are developing around the world, concentrating expertise and knowledge from China and Japan to the United States and across Europe. Networked care is scaling up as an effective model of care for rare diseases, with prevention, diagnosis, care and treatment administered locally, informed by the body of knowledge and expertise from the whole network. Now, as the United Nations encourages the development of rare disease networks in all countries, it is timely to reflect on the key characteristics of an effective network. This article aims to identify the core themes needed for a clinical network to be healthy. This article drawing on experience from existing networks through a series of semi-structured interviews, insights from leaders of existing networks are then triangulated with the published evidence. The review aims to identify the themes that allow a clinical network to be effective and flourish. Healthcare networks are best understood as learning systems to generate collaborative knowledge used to inform the best possible care. Six themes are consistently reported in the literature and leaders’ experience: Trust, Communication, Leadership, Learning, Diversity and Resources. Learning together is a key element of the success of effective networks and is most effective when networks are professionally multi-cultural and diverse, including the voices of people living with a rare disease. Patient representative involvement is fundamental to network collaboration and is recognized as a key aspect of early successes. Clinical leadership is critical to providing legitimacy and trust, creating a common identity and promoting collaboration. Networks take time, resources and coordination to develop. Although in-kind support and voluntary contributions of network members are important, inadequate resourcing is a critical barrier to the long-term sustainability and effectiveness of networks. This review explores the core themes of effective networks. Through harnessing digital solutions that enable experts to coordinate care virtually across a clinical network, healthcare for people living with a rare disease is evolving to meet their complex needs. However, payment models to finance these models of care still lag behind innovative healthcare delivery models.

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Iturria-Rivera, Pedro Enrique, Han Zhang, Hao Zhou, Shahram Mollahasani, and Melike Erol-Kantarci. "Multi-Agent Team Learning in Virtualized Open Radio Access Networks (O-RAN)." Sensors 22, no. 14 (July 19, 2022): 5375. http://dx.doi.org/10.3390/s22145375.

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Starting from the concept of the Cloud Radio Access Network (C-RAN), continuing with the virtual Radio Access Network (vRAN) and most recently with the Open RAN (O-RAN) initiative, Radio Access Network (RAN) architectures have significantly evolved in the past decade. In the last few years, the wireless industry has witnessed a strong trend towards disaggregated, virtualized and open RANs, with numerous tests and deployments worldwide. One unique aspect that motivates this paper is the availability of new opportunities that arise from using machine learning, more specifically multi-agent team learning (MATL), to optimize the RAN in a closed-loop where the complexity of disaggregation and virtualization makes well-known Self-Organized Networking (SON) solutions inadequate. In our view, Multi-Agent Systems (MASs) with MATL can play an essential role in the orchestration of O-RAN controllers, i.e., near-real-time and non-real-time RAN Intelligent Controllers (RIC). In this article, we first provide an overview of the landscape in RAN disaggregation, virtualization and O-RAN, then we present the state-of-the-art research in multi-agent systems and team learning as well as their application to O-RAN. We present a case study for team learning where agents are two distinct xApps: power allocation and radio resource allocation. We demonstrate how team learning can enhance network performance when team learning is used instead of individual learning agents. Finally, we identify challenges and open issues to provide a roadmap for researchers in the area of MATL based O-RAN optimization.

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Zogan, Hamad, Imran Razzak, Xianzhi Wang, Shoaib Jameel, and Guandong Xu. "Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media." World Wide Web 25, no. 1 (January 2022): 281–304. http://dx.doi.org/10.1007/s11280-021-00992-2.

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AbstractThe ability to explain why the model produced results in such a way is an important problem, especially in the medical domain. Model explainability is important for building trust by providing insight into the model prediction. However, most existing machine learning methods provide no explainability, which is worrying. For instance, in the task of automatic depression prediction, most machine learning models lead to predictions that are obscure to humans. In this work, we propose explainable Multi-Aspect Depression Detection with Hierarchical Attention Network MDHAN, for automatic detection of depressed users on social media and explain the model prediction. We have considered user posts augmented with additional features from Twitter. Specifically, we encode user posts using two levels of attention mechanisms applied at the tweet-level and word-level, calculate each tweet and words’ importance, and capture semantic sequence features from the user timelines (posts). Our hierarchical attention model is developed in such a way that it can capture patterns that leads to explainable results. Our experiments show that MDHAN outperforms several popular and robust baseline methods, demonstrating the effectiveness of combining deep learning with multi-aspect features. We also show that our model helps improve predictive performance when detecting depression in users who are posting messages publicly on social media. MDHAN achieves excellent performance and ensures adequate evidence to explain the prediction.

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Sujatha. "Classical Flexible Lip Model Based Relative Weight Finder for Better Lip Reading Utilizing Multi Aspect Lip Geometry." Journal of Computer Science 6, no. 10 (October 1, 2010): 1065–69. http://dx.doi.org/10.3844/jcssp.2010.1065.1069.

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Journal articles on the topic 'Multi-aspect networks'

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