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Adversarial Graph Convolutional Network Hashing for Cross-Modal Retrieval 7th

Adversarial Graph Convolutional Network Hashing for Cross-Mo...

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8th Asia Pacific Web and Web-Age information Management Joint International Conference on Web and Big Data

作者： Lu, Bo Zhao, Tianbao Liang, Guiyuan Li, Jiaming Duan, Xiaodong Dalian Minzu Univ Dalian 116650 Peoples R China

ISBN: (纸本)9789819600540;9789819600557

Graph-based hashing has recently emerged as a prominent research area due to the rapid expansion of multimodal datasets. Most existing graph-based methods rely on Graph Convolutional Networks (GCNs) as a guiding mechanism, primarily because of the unique characteristics of cross-modal retrieval tasks. However, these methods often encounter several challenges: 1) They struggle to preserve the inherent similarities within the original datasets. 2) They frequently employ intractable binary quadratic programs, which limits their scalability when dealing with extensive data volumes. In this paper, we introduce an innovative framework termed Adversarial Graph Convolutional Network Hashing (AGCNH), which leverages dual-cycle Generative Adversarial Networks (GANs) alongside GCNs. This framework incorporates two asymmetric graph layers and a standard graph layer, effectively addressing the issue of excessive parameterization in GCNs and enabling the direct generation of binary codes. To demonstrate the effectiveness of our proposed method, we have conducted experiments on four widely used datasets, thereby validating the robustness and applicability of our approach.

关键词： Cross-Modal Retrieval Adversarial Graph Convolutional Network Hashing

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Towards a Neurofeedback Tool For Emotion Recognition Using Brain computer Interface 4th

Towards a Neurofeedback Tool For Emotion Recognition Using B...

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24th International Conference Web Engineering (ICWE)

作者： Ardito, Carmelo Colafiglio, Tommaso Di Noia, Tommaso Lombardi, Angela Lofu, Domenico Narducci, Fedelucio Sorino, Paolo Politecn Bari Dept Elect & Informat Engn DEI Bari Italy Sapienza Univ Roma Dept Comp Automat & Management Engn DIAG Rome Italy Univ LUM Giuseppe Degennaro Dept Engn I-70010 Casamassima BA Italy

ISBN: (纸本)9783031751097;9783031751103

Brain-computer Interface (BCI) allows machines to be controlled by signals derived from EEG analysis. Several low-cost electroencephalographs are now available on the market, all of which provide highquality Electroencephalogram (EEG) signals. Detecting a user's emotional state by analysing their EEG signal is a fascinating approach in this field. In our research, we have attempted to detect the emotional state of nine different emotions. Our research presents a working prototype of an EEG-based emotion recognizer that can be used as biofeedback to provide information about the user's emotional state.

关键词： Brain-computer Interfaces Bio-Feedback Emotion Recognition Electroencephalographic Machine Learning

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Many-Objective Artificial Bee Colony Algorithm Based on Dual Indicators 5th

Many-Objective Artificial Bee Colony Algorithm Based on Dual...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Zhang, Shaowei Xiao, Dong Liao, Futao Wang, Hui Hu, Min Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Jiangxi Peoples R China

ISBN: (纸本)9789819770038;9789819770045

Artificial bee colony (ABC) is a well-known swarm intelligence algorithm that has been widely applied to many optimization problems. However, ABC shows some shortcomings in tackling many-objective optimization problems (MaOPs). In this paper, a novel many-objective ABC based on dual indictors (called DIMABC) is proposed to solve MaOPs. In DIMABC, a convergence indicator based on favorable weights is used to guide the search, mating selection and environmental selection. Based on the convergence indicator, good solutions are selected to construct an elite set, which is used to construct a new search strategy. In addition, the probability selection model is also improved based on the convergence indicator in the onlooker bee stage. Finally, a new diversity indicator and the convergence indicator are used in the environmental selection to make a balance between convergence and diversity. To validate the optimization capability of the proposed DIMABC, nine WFG benchmark problems with 3, 5, 8, and 15 objectives are utilized. Experimental results show that DIMABC obtains superior performance when compared with five other well-established algorithms.

关键词： Artificial bee colony algorithm Swarm intelligence Many-objective optimization Environment selection

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Deep Learning Approach for Tunisian Postal Address Segmentation 4th

Deep Learning Approach for Tunisian Postal Address Segmentat...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Bouafif, Fadoua Abdellatif, Linda Univ Carthage FSEGN LR SITI Campus Univ Mrezga Hammamet Nabeul Carthage Tunisia Univ Tunis ENSIT LaTICE 5 Ave Taha Hussein Tunis Bab Mnara Tunisia

ISBN: (纸本)9783031821523;9783031821530

Automatic document recognition relies heavily on accurate information localization and extraction, especially in offline Arabic handwritten document processing. Many postal systems in Arabic countries, like Tunisia, still rely on manual mail sorting and processing due to the challenges of Arabic script recognition. These challenges include the inherent cursive nature of the script, context dependence of characters, segmentation difficulties, and a scarcity of deep learning-based solutions. This paper proposes a deep learning approach for segmenting handwritten Tunisian postal addresses. Our method utilizes a customized Faster Regional-Convolutional Neural Network (Faster R-CNN) architecture to achieve high detection accuracy for various postal address components.

关键词： Faster-RCNN Segmentation postal address

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Using of Ellipsoid Method for Finding Linear Regression Parameters with L1-Regularization 2nd

Using of Ellipsoid Method for Finding Linear Regression Para...

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2nd International Conference on information Technologies and Their Applications

作者： Stetsyuk, Petro Stovba, Viktor Korablov, Mykola VM Glushkov Cybernet Inst NASU Kiev Ukraine

ISBN: (纸本)9783031734168;9783031734175

The problem of parameters determination of a linear regression model with the least moduli criterion powered to 1 <= p <= 2 with L-1-regularization is investigated. It corresponds to the problem of unconditional minimization of a non-smooth convex piecewise linear function. The formulation of the problem with p = 1 and the linear programming problem corresponding to it are given. For determination of model parameters, the emlmpr algorithm is proposed, which implements the Shor's ellipsoid method. The Octave-code of the algorithm and the results of three computational experiments are given. The first experiment is designed to estimate the amount of time needed to solve the problem. The purpose of the second experiment is to demonstrate the robustness of the least moduli method. The third experiment is designed to investigate the effect of L-1-regularization on parameters determination of a model, in which there are linear relationships between factors. The effect of the regularization parameter value on the quality of the model parameters determination with different coefficients for dependent factors is analyzed.

关键词： Linear Regression Dependent Factors Least Moduli Criterion L-1-regularization Non-smooth Optimization Problem Shor's Ellipsoid Method

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SDR-Based Surveillance Systems to Improve the Safety and Security of Border Farmlands 2nd

SDR-Based Surveillance Systems to Improve the Safety and Sec...

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2nd International Conference on Agriculture-Centric Computation

作者： Singh, J. Karan Khairnar, C. N. Mil Coll Telecommun Engn Fac Commun Engn Indore Madhya Pradesh India

ISBN: (纸本)9783031744396;9783031744402

Indian Agriculture is witnessing a steep decline in the contribution to the country's GDP with a meager 15% contribution to nation-building recorded in 2011. Although, Punjab carries a historical reputation as India's "breadbasket," its geographical proximity to the Golden Crescent route has made it a lucrative destination for drug smuggling activities. This has led to massive addiction among its youth resulting in compromised agriculture activities. Despite increased surveillance along the International Boundary by the national security forces, the smugglers have innovated the use of Drones in cross-border transport of narcotics. The deployment of Software Defined Radio-based surveillance systems along border farmlands can serve as a primary alarm system to alert the farmers and security forces of an incoming rogue drone, thus promising enhanced border security and relief from Drug Menace.

关键词： Drones Narcotics GNU Radio Software Defined Radio (SDR)

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Edge-Cloud Solution Based on FIWARE and Context Augmentation to Monitor Usages of Carpooling Car Parks 1st

Edge-Cloud Solution Based on FIWARE and Context Augmentation...

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1st Global Internet of Things and Edge Computing Summit

作者： Orazi, Gilles Marot, Marianne Khelifi, Iheb Le Gall, Franck Richard, Amara Martzluff, Yvan EGM AREP Ctr Traverse Brucs F-06560 Valbonne France Conseil Dept Pyrenees Orient Perpignan France

ISBN: (纸本)9783031785719;9783031785726

Monitoring the usage and carbon impact of infrastructures such as carpooling car parks may be a challenge for local authorities. In this study, we address this issue by proposing an IoT solution that integrates both Edge processing and Cloud computing. Real-time continuous video AI processing automatically detects and stores information of car entries and exits. Then Cloud computing stores the anonymized data into a FIWARE information broker using ETSI's NGSI-LD specification and employs query language to process and display useful statistics about carpark usage. To understand the carpooling user behaviors for each car park, as well as the various Key Performance Indicators and threshold values that direct our monitoring and alerting systems, we analyzed 1 year of data acquisition and performed in-situ surveys. Hereby, we present our results that reflect two different usages of carpooling (professional and personal) in terms of stay durations, car fidelity, car energy distributions, and carbon impact. This solution was deployed and tested on 3 car park lots, 2 of which have been permanently running for about two years.

关键词： Monitoring Mobility Outlier detection Data Quality Decarbonation GHG emission reduction FIWARE NGSI-LD

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Real Time Traffic Management by Optimizing Level of Service 4th

Real Time Traffic Management by Optimizing Level of Service

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4th International Conference on Artificial Intelligence and Knowledge Processing

作者： Garg, Tanya Kaur, Gurjinder Rana, Prashant Singh Wani, Niyaz Ahmad St Longowal Inst Engn & Technol Longowal Punjab India Thapar Inst Engn & Technol Patiala Punjab India

ISBN: (纸本)9783031734762;9783031734779

The multifaceted discipline of traffic management is essential for coordinating the flow of cars, pedestrians, and other forms of transportation in metropolitan areas. In the context of smart transportation, signalized intersection simulation is essential. It uses sophisticated modeling approaches to forecast traffic flow, optimize signal timings, and improve intersection efficiency. In this paper, we proposed an efficient traffic management approach for the real time traffic for the Patiala city. The real time traffic data has been collected for the peak hours for the area near new bus stand, Patiala. Then the collected data has been analyzed and simulations for the collected data has been done using PTV VISSIM (Planung Transport Verkehr Vissim), one of the best microscopic and 3D traffic simulation tools. Furthermore, the work aimed to study the Level of Service (LOS), flow speed density, and traffic simulation of the intersection, analyze the data and findings to identify deficiencies, and propose effective solutions to enhance road safety [1]. The results show that our proposed approach successfully reduced the congestion length on roads and improved level of service.

关键词： Data collection Level of Service (LoS) Queue length Road safety Signalized intersections Traffic management

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VidLyze: Video Content Toxicity Identification Tool Using Natural Language Processing (NLP) and Machine Learning 5th

VidLyze: Video Content Toxicity Identification Tool Using Na...

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5th International Symposium on Intelligent Computing Systems

作者： Alfalasi, Ahmad Obaid Matar, Mohammad Sameer, Mohammad Siddartha, Sharmila Hudaya, Anang Amin, Muhamad Dubai Campus Acad City Fac Comp & Informat Sci Higher Coll Technol Dubai U Arab Emirates

ISBN: (纸本)9783031829307;9783031829314

Toxicity identification and classification in multimedia contents is becoming an important field of research, particularly with the massive amount of multimedia contents that are produced and published on the web. Toxic multimedia content such as the use of inappropriate and bad words and swearing are not suitable for general audiences, especially for young children who are highly impressionable. In this paper, we present our current work-in-progress on designing and developing analytics model known as VidLyze for identifying different levels of toxicity in a given video, based on the text analysis approach. The goal of VidLyze is to use machine learning and natural language processing (NLP) to make a system that can watch and analyze video material for toxic contents. The goal is to find toxic, inappropriate or insulting content in videos so that people can watch them safely. The work carried out involved extracting video contents with different formats, converting audio to text in order to obtain the audio transcript, and performing text toxicity analysis on the audio transcript using a suitable machine learning algorithm. The proposed analytical model requires NLP integration for analyzing human languages from video and audio, and the need for datasets that are large enough to train the respective model. We adopt the Long Short-Term Memory (LSTM) recurrent neural networks to train the model for classifying toxicity based on large textual datasets. VidLyze is made to be a safe and reliable method that makes content moderation better and makes sure everyone has a good time watching.

关键词： Video Analytics Speech Recognition Machine Learning

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Learning to Design Greedy Algorithm for NP-Complete Problems 42nd

Learning to Design Greedy Algorithm for NP-Complete Problems

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42nd National Conference on Theoretical computer science

作者： Ding, Zhenxin Huang, Zihao Sui, Jingyan Liu, Ruizhi Ding, Shizhe Xu, Liming Wang, Chao Zhang, Haicang Yu, Chungong Bu, Dongbo Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9789819614899;9789819614905

Algorithm design is an art that heavily requires human designers' intuition, expertise, and insights into the combinatorial structure of the problems under consideration. In particular, while greedy algorithms for NP-complete problems are generally straightforward, finding an effective greedy-selection rule is consistently challenging. In the study, we introduce an approach called artificial intelligence algorithmist for set cover problem (AIA-SC), which leverages neural networks as greedy-selection rules to solve the minimum weighted set cover problem (WSCP), an NP-complete problem. Initially, we formulate a given WSCP as a 0-1 integer linear program (ILP), in which each variable xi is binary: x(i) = 0 indicating the exclusion of set s(i), and x(i) = 1 indicating the selection of the set. Subsequently, we design a generic search framework to identify the optimal solution to the ILP. At each search step, the value of a variable is determined with the aid of neural networks. The key ingredients of our neural network involve the design of its loss function and training process: the original ILP problem and the sub-problems generated by assigning a variable x(i) should satisfy the Bellman-Ford equation, which enables us to set the violation of Bellman-Ford equation as loss function of our neural network. Experimental results on representative instances indicate that the neural network-based greedy selection rule effectively identifies optimal solutions and surpasses the performance of the human-crafted Chvatal's greedy algorithm. Furthermore, the basic idea of our approach can be readily extended to design greedy algorithms for other NP-hard problems without significant modification. The source code of AIA-SC and data sets are available at https://***/zxding94/aia-sc.

关键词： Algorithm Design Greedy-selection Rules Neural Networks Minimum Weighted Set Cover Problem

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