scholarly journals Semantic-guided autoencoder adversarial hashing for large-scale cross-modal retrieval

2022 ◽  
Author(s):  
Mingyong Li ◽  
Qiqi Li ◽  
Yan Ma ◽  
Degang Yang
Keyword(s):  
Large Scale ◽  
Research Work ◽  
Feature Learning ◽  
Data Retrieval ◽  
Mobile Internet ◽  
Multimedia Data ◽  
Internet Technology ◽  
Learning Ability ◽  
Smart Devices ◽  
Network Training

AbstractWith the vigorous development of mobile Internet technology and the popularization of smart devices, while the amount of multimedia data has exploded, its forms have become more and more diversified. People’s demand for information is no longer satisfied with single-modal data retrieval, and cross-modal retrieval has become a research hotspot in recent years. Due to the strong feature learning ability of deep learning, cross-modal deep hashing has been extensively studied. However, the similarity of different modalities is difficult to measure directly because of the different distribution and representation of cross-modal. Therefore, it is urgent to eliminate the modal gap and improve retrieval accuracy. Some previous research work has introduced GANs in cross-modal hashing to reduce semantic differences between different modalities. However, most of the existing GAN-based cross-modal hashing methods have some issues such as network training is unstable and gradient disappears, which affect the elimination of modal differences. To solve this issue, this paper proposed a novel Semantic-guided Autoencoder Adversarial Hashing method for cross-modal retrieval (SAAH). First of all, two kinds of adversarial autoencoder networks, under the guidance of semantic multi-labels, maximize the semantic relevance of instances and maintain the immutability of cross-modal. Secondly, under the supervision of semantics, the adversarial module guides the feature learning process and maintains the modality relations. In addition, to maintain the inter-modal correlation of all similar pairs, this paper use two types of loss functions to maintain the similarity. To verify the effectiveness of our proposed method, sufficient experiments were conducted on three widely used cross-modal datasets (MIRFLICKR, NUS-WIDE and MS COCO), and compared with several representatives advanced cross-modal retrieval methods, SAAH achieved leading retrieval performance.

scholarly journals Impact of Perceived Risk on Consumers Technology Acceptance in Online Grocery Adoption amid COVID-19 Pandemic

2021 ◽  
Vol 13 (18) ◽  
pp. 10221
Author(s):  
Sufyan Habib ◽  
Nawaf N. Hamadneh
Keyword(s):  
Perceived Risk ◽  
Purchase Intention ◽  
Online Shopping ◽  
Research Work ◽  
Mobile Internet ◽  
Growth Potential ◽  
Internet Technology ◽  
Consumer Trust ◽  
Online Purchase ◽  
The Impact

E-commerce industry has witnessed a phenomenal growth globally due to the sudden spread of the COVID-19 pandemic and the advancement of mobile Internet technology, with fast adaption of online shopping technologies by the customers. Previously, online shopping was only available in a few product categories and to a select group of consumers. The COVID-19 guidelines related to safety, physical distancing, closure, lockdown, and other restrictions have insisted that consumers shop online. Because of e-commerce growth, the grocery (FMCG) industry is also equipped with advanced technologies such as the Internet of Things (IoT), cloud computing, and block chain technology. This paper analyzes the UTAUT2 model and its influence on perceived risk and consumer trust in online purchase intention of grocery categories of products among Indian customers. We tried to analyze the growth potential of new technologies in grocery retail and formulated the hypotheses. The results showed that the spread of COVID-19 pandemic had a significant influence on the online shopping behavior of Indian customers. The outcome of the study partly assists businesses in understanding the impact of the factors of consumer adaption of technology, perceived risk associated with online transaction, consumer trust in online technologies and consumer online purchase intention of grocery products. To promote e-commerce in India, the current study suggests that marketers should try to develop consumer trust and lowering the perceived risk associated with online shopping. Some management implications and future area of study based on empirical findings are also highlighted in the present research work.

Biological Data Classification and Analysis Using Convolutional Neural Network

2020 ◽  
Vol 10 (10) ◽  
pp. 2459-2465
Author(s):  
Iftikhar Ahmad ◽  
Muhammad Javed Iqbal ◽  
Mohammad Basheri
Keyword(s):  
Deep Learning ◽  
Early Diagnosis ◽  
Language Processing ◽  
Large Scale ◽  
Genetic Diseases ◽  
Research Work ◽  
Biological Data ◽  
Learning Ability ◽  
Learning Models ◽  
Depth Analysis

The size of data gathered from various ongoing biological and clinically studies is increasing at an exponential rate. The bio-inspired data mainly comprises of genes of DNA, protein and variety of proteomics and genetic diseases. Additionally, DNA microarray data is also available for early diagnosis and prediction of various types of cancer diseases. Interestingly, this data may store very vital information about genes, their structure and important biological function. The huge volume and constant increase in the extracted bio data has opened several challenges. Many bioinformatics and machine learning models have been developed but those fail to address key challenges presents in the efficient and accurate analysis of variety of complex biologically inspired data such as genetic diseases etc. The reliable and robust process of classifying the extracted data into different classes based on the information hidden in the sample data is also a very interesting and open problem. This research work mainly focuses to overcome major challenges in the accurate protein classification keeping in view of the success of deep learning models in natural language processing since it assumes the proteins sequences as a language. The learning ability and overall classification performance of the proposed system can be validated with deep learning classification models. The proposed system can have the superior ability to accurately classify the mentioned datasets than previous approaches and shows better results. The in-depth analysis of multifaceted biological data may also help in the early diagnosis of diseases that causes due to mutation of genes and to overcome arising challenges in the development of large-scale healthcare systems.

scholarly journals Exploring the Evolution of New Mobile Services

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yong Chen ◽  
Juncheng Yao ◽  
Hai Jin ◽  
Chunjiang He ◽  
Hanhua Chen
Keyword(s):  
Mobile Communications ◽  
Large Scale ◽  
Service Providers ◽  
Evolutionary Game ◽  
Real Data ◽  
Mobile Internet ◽  
Internet Technology ◽  
Mobile Service ◽  
Communications Services ◽  
Real World Datasets

The emergence and widespread use of mobile Internet technology has led to many different kinds of new mobile communications services, such asWeChat. Users could have more choices when attempting to satisfy their communications needs. The ability to predict the way in which users will use new mobile communications services is extremely valuable to mobile communications service providers. In this work, we propose a method for predicting how a user will use a new mobile service. Our scheme is inspired by the evolutionary game theory. With large-scale real world datasets collected from mobile service providers, we first extract the benefit-related features for users who were starting to use a new mobile service. Then we design our training and prediction methods for predicting potential users. We evaluate our scheme using experiments with large-scale real data. The results show that our approach can predict users’ future behavior with satisfying accuracy.

Biological Data Classification and Analysis Using Convolutional Neural Network

2020 ◽  
Vol 10 (10) ◽  
pp. 2459-2465
Author(s):  
Iftikhar Ahmad ◽  
Muhammad Javed Iqbal ◽  
Mohammad Basheri
Keyword(s):  
Deep Learning ◽  
Early Diagnosis ◽  
Language Processing ◽  
Large Scale ◽  
Genetic Diseases ◽  
Research Work ◽  
Biological Data ◽  
Learning Ability ◽  
Learning Models ◽  
Depth Analysis

The size of data gathered from various ongoing biological and clinically studies is increasing at an exponential rate. The bio-inspired data mainly comprises of genes of DNA, protein and variety of proteomics and genetic diseases. Additionally, DNA microarray data is also available for early diagnosis and prediction of various types of cancer diseases. Interestingly, this data may store very vital information about genes, their structure and important biological function. The huge volume and constant increase in the extracted bio data has opened several challenges. Many bioinformatics and machine learning models have been developed but those fail to address key challenges presents in the efficient and accurate analysis of variety of complex biologically inspired data such as genetic diseases etc. The reliable and robust process of classifying the extracted data into different classes based on the information hidden in the sample data is also a very interesting and open problem. This research work mainly focuses to overcome major challenges in the accurate protein classification keeping in view of the success of deep learning models in natural language processing since it assumes the proteins sequences as a language. The learning ability and overall classification performance of the proposed system can be validated with deep learning classification models. The proposed system can have the superior ability to accurately classify the mentioned datasets than previous approaches and shows better results. The in-depth analysis of multifaceted biological data may also help in the early diagnosis of diseases that causes due to mutation of genes and to overcome arising challenges in the development of large-scale healthcare systems.

Guest Editorial: Large-Scale Multimedia Data Retrieval, Classification, and Understanding

2017 ◽  
Vol 19 (9) ◽  
pp. 1965-1967 ◽  
Author(s):  
J. Song ◽  
H. Jegou ◽  
C. Snoek ◽  
Q. Tian ◽  
N. Sebe
Keyword(s):  
Large Scale ◽  
Guest Editorial ◽  
Data Retrieval ◽  
Multimedia Data

A novel MapReduce-based deep convolutional neural network algorithm

2021 ◽  
pp. 1-13
Author(s):  
Xiang-Min Liu ◽  
Jian Hu ◽  
Deborah Simon Mwakapesa ◽  
Y.A. Nanehkaran ◽  
Yi-Min Mao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Big Data ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Feature Learning ◽  
Deep Convolutional Neural Networks ◽  
Network Training ◽  
Load Rate ◽  
Data Environment ◽  
Neural Networks Optimization

Deep convolutional neural networks (DCNNs), with their complex network structure and powerful feature learning and feature expression capabilities, have been remarkable successes in many large-scale recognition tasks. However, with the expectation of memory overhead and response time, along with the increasing scale of data, DCNN faces three non-rival challenges in a big data environment: excessive network parameters, slow convergence, and inefficient parallelism. To tackle these three problems, this paper develops a deep convolutional neural networks optimization algorithm (PDCNNO) in the MapReduce framework. The proposed method first pruned the network to obtain a compressed network in order to effectively reduce redundant parameters. Next, a conjugate gradient method based on modified secant equation (CGMSE) is developed in the Map phase to further accelerate the convergence of the network. Finally, a load balancing strategy based on regulate load rate (LBRLA) is proposed in the Reduce phase to quickly achieve equal grouping of data and thus improving the parallel performance of the system. We compared the PDCNNO algorithm with other algorithms on three datasets, including SVHN, EMNIST Digits, and ISLVRC2012. The experimental results show that our algorithm not only reduces the space and time overhead of network training but also obtains a well-performing speed-up ratio in a big data environment.

Mobile Internet Oriented M-Learning System

2013 ◽  
Vol 411-414 ◽  
pp. 2883-2887
Author(s):  
Jie Mei Lin ◽  
Rong Huang ◽  
Jia Yin Zhao ◽  
Qing Dai
Keyword(s):  
Mobile Learning ◽  
Large Scale ◽  
Mobile Internet ◽  
Learning System ◽  
Smart Devices ◽  
Key Issues ◽  
Access Information ◽  
Smart Mobile ◽  
Mobile Learning System ◽  
Future Learning

In recent years, the mobile internet is deployed rapidly in large-scale. Meanwhile the smart mobile devices are penetrated universally. The combination of them provides the sufficient precondition for the Mobile Learning, i.e. M-Learning. A revolution on future learning foreseen because of the M-learning, characterized with mobility, convenience, timeliness and other characteristics, enabling anywhere, anytime learning for anybody via smart devices in order to easier access information, flexible attend classes, and freely join discussion, etc. This paper analyzes the key elements and characteristics of mobile Internet-oriented mobile learning system, provides the framework of M-Learning system functionality architecture for core applications, and furthermore focuses on some technical key issues including knowledge aggregation/mashup and information pushing for mobile learning.

scholarly journals Deep Unsupervised Hashing for Large-Scale Cross-Modal Retrieval Using Knowledge Distillation Model

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Mingyong Li ◽  
Qiqi Li ◽  
Lirong Tang ◽  
Shuang Peng ◽  
Yan Ma ◽  
...  
Keyword(s):  
Large Scale ◽  
Data Retrieval ◽  
Multimedia Data ◽  
Search Performance ◽  
Similarity Matrix ◽  
Student Model ◽  
Deep Hashing ◽  
Knowledge Distillation ◽  
Semantic Alignment ◽  
Teacher Model

Cross-modal hashing encodes heterogeneous multimedia data into compact binary code to achieve fast and flexible retrieval across different modalities. Due to its low storage cost and high retrieval efficiency, it has received widespread attention. Supervised deep hashing significantly improves search performance and usually yields more accurate results, but requires a lot of manual annotation of the data. In contrast, unsupervised deep hashing is difficult to achieve satisfactory performance due to the lack of reliable supervisory information. To solve this problem, inspired by knowledge distillation, we propose a novel unsupervised knowledge distillation cross-modal hashing method based on semantic alignment (SAKDH), which can reconstruct the similarity matrix using the hidden correlation information of the pretrained unsupervised teacher model, and the reconstructed similarity matrix can be used to guide the supervised student model. Specifically, firstly, the teacher model adopted an unsupervised semantic alignment hashing method, which can construct a modal fusion similarity matrix. Secondly, under the supervision of teacher model distillation information, the student model can generate more discriminative hash codes. Experimental results on two extensive benchmark datasets (MIRFLICKR-25K and NUS-WIDE) show that compared to several representative unsupervised cross-modal hashing methods, the mean average precision (MAP) of our proposed method has achieved a significant improvement. It fully reflects its effectiveness in large-scale cross-modal data retrieval.

scholarly journals Automatic Algorithm for Fractal Plant Art Image Similarity Feature Generation

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhizhe Liu ◽  
Luo Sun
Keyword(s):  
Large Scale ◽  
Service Providers ◽  
Cloud Service ◽  
Multimedia Data ◽  
Image Feature ◽  
Image Similarity ◽  
Smart Devices ◽  
Scale Invariant ◽  
Image Security ◽  
The Impact

With the popularity of smart devices and the Internet, the volume of multimedia data is growing rapidly, and content-based image retrieval (CBIR) can search for similar images from large-scale images to realize the utilization of the data. For data owners, outsourcing the management and maintenance of image data to cloud service providers can effectively reduce costs, but there is a privacy leakage problem. In this paper, we focus on image feature extraction, index design, and image similarity recognition methods under a dual server model with content-based image security similarity recognition as the research topic, the work done such as proposing a BOVW (Bag of Visual Word) feature-based image security similarity recognition scheme. The scheme combines SIFT (scale-invariant feature transform) feature secure extraction and locally sensitive hashing algorithm to achieve secure extraction of BOVW features of images. To protect the BOVW features of images, an inverted index based on word frequency division is designed, the index is stored in chunks, and an image secure similarity recognition scheme based on CNN (convolutional neural networks) features is proposed. The scalable hash index based on dimensional division is designed based on the image CNN features secure extraction algorithm. The security and performance of the proposed scheme are theoretically analyzed and experimentally verified. Based on different image datasets, the impact of different parameters on the performance of the scheme is tested, and optimized parameters are given. The experimental results show that the proposed scheme in this paper can effectively improve the efficiency of analyzing the similarity of plant botanical art images compared to the existing schemes.

scholarly journals Ranked k-NN Crowdsourced Model for Cloud Internet of Things (CIoT)

2020 ◽  
Vol 2 (3) ◽  
pp. 173-180
Author(s):  
Dr. M. Duraipandian
Keyword(s):  
Internet Of Things ◽  
Large Scale ◽  
Research Work ◽  
Future Internet ◽  
Smart Devices ◽  
Analysis Model ◽  
Time Data ◽  
Efficient Manner ◽  
Computing Paradigm ◽  
Sensitivity Specificity

Internet of Things (IoT) has gained more attention in recent years and its influence over future internet is projected to be more as a promising technology. IoT enables sensors to merge with smart devices to monitor, observe and analyse the real time data. These features make the IoT, a suitable technology, for smart applications. On the other hand, cloud offers a better computing paradigm to store and analyse the data. Cloud reduces the complexities in day today life with its novel applications and services, in an efficient manner. However, present IoT and Cloud solutions are focused towards centralized solutions, which limits the user capacity. To enrich the Cloud integrated IoT benefits, a flexible large-scale data collection and analysis is introduced as crowdsourcing, which provides a new dimension in data mining applications. This research work presents a cloud computing crowdsourced data analysis model implemented over IoT, to obtain better computation speed with improved sensitivity, specificity and accuracy.

Sign in / Sign up

Export Citation Format

Share Document