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Weak Devices Matter: Model Heterogeneity in Resource-Constrained Federated Learning

Weak Devices Matter: Model Heterogeneity in Resource-Constra...

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IEEE International conference on Communications (ICC)

作者： Tzu–Hsuan Peng Cheng-Wei Huang Ai-Chun Pang Te-Chuan Chiu Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan R.O.C Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan R.O.C High Performance and Scientific Computing Center National Taiwan University Taipei Taiwan R.O.C Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan R.O.C Department of Computer Science National Tsing Hua University Hsinchu Taiwan R.O.C

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Federated Learning (FL) is a promising framework for enabling edge intelligence while preserving privacy and enhancing communication efficiency. In real-world scenarios, limited and heterogeneous hardware resources among users threaten the overall training efficiency in FL. To address this issue, several model heterogeneity frameworks have emerged in recent years. However, existing studies primarily focus on mitigating idle times on powerful devices, overlooking the fact that strong devices may not always have a positive impact and can potentially become a burden instead. Furthermore, as the resource gap among clients widens or the number of weak devices increases, the model may be hindered by the influence of strong devices. Directly applying existing model heterogeneity frameworks leads to model performance degradation due to the neglect of contributions from weak devices. To make a performance breakthrough, we proposed a novel FL framework called Progressive Width Mixing (PWM), which extracts diverse features and balances the contribution of strong and weak devices and mitigates straggler effects under scenarios with various device distributions. To our best knowledge, we are the first model heterogeneity work that considered straggler effects from the weak devices' point of view. The experiments show that PWM maintains stability over different datasets and outperforms all baselines across various device distributions and non-IID scenarios. PWM makes a leap forward and surpasses all baselines by 7%, and mitigates the negative impact from the strong devices.

关键词： Performance evaluation Training Privacy Philosophical considerations Federated learning Pulse width modulation System recovery

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Image Captioning Using Machine/Deep Learning

Image Captioning Using Machine/Deep Learning

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Advances in computing, Communication Control and networking (ICAC3N), International conference on

作者： Vishal Singh Ajay Shankar Singh K Anandhan Computer Science and Engineering Galgotias University Greater Noida India School of Computer Science and Engineering Galgotias University Greater Noida India

The System that automatically write descriptions or write captions for an image according to the contents of that image is known as Auto Image Captioning. It’s literacy problem of the machine that combines computer vision (for understanding image contents) with Natural processing (for textbook product) Auto picture captioning is a fairly new and fleetly adding academic content. Colourful new styles are being launched on a diurnal base in order to attain satisfying outcomes in this discipline. Still, to gain mortal- suchlike results, a lot of attention is still necessary. The thing of this exploration is to discover, in a methodical manner, what different and recent methodologies and models are employed for picture captioning using deep learning. What approach and new ideas are used to make these models better and more accurate. what’s further, ideas and strategy are more likely to give suitable result or outcome. To get so, we did a methodical literature analysis of recent exploration published between 2017 and 2019 from well- known datasource (Scopus, Web of Lores, IEEEXplore). We plant an aggregate of over 60+ primary studies that were related to this study. We found out that CNN understands the content in the picture and detect objects in an image, whereas LSTM/RNN is used to induce language. MS COCO, flicker 8k and flicker 30k, which are used in almost every studies, are the most extensively used datasets. The BLEU (1 – 4) standing rating matrix is the most extensively utilised in all projects or studies. The analysis also show that CNN with LSTM is more preferable than CNN with RNN. We found out that the Encoder Decoder and the attention medium are one of the most suitable and best ways for enforcing this model, and combining them can ameliorate outcomes to more accurate and good level. Experimenters or other people that want to work or contribute to auto image captioning will surely find this exploration very useful and can use it as a guidance.

关键词： Deep learning Computer vision Image databases Aggregates Neural networks multimedia Web sites Network architecture

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Deep Computational Based Blocking Effect Cancellation for Digital Image Compression

Deep Computational Based Blocking Effect Cancellation for Di...

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International conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： D Ferlin Deva Shahila Valantina Stephen R Senthil Rama Banu Priya Prathaban Jency Rubia J Sura Rakesh Reddy Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of EEE Saveetha School of Engineering Thandalam Chennai India Department of EEE DMI College of Engineering Chennai Department of Networking and Communication School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

Large multimedia-based applications need that academics find a more effective approach to use images in their apps as image data continues to grow. Image quality evaluation is crucial in image processing systems, especially when images are compressed for transmission. Because of the blocking effect that co-efficient quantification causes during image compression, the decompressed image has poor quality. The coefficient quantification of blocking effect removes high frequency component in the image, consecutively results in discontinuous leaps and reduces the quality of the image. Generally, the objective function is used to perform optimization on complex image processing task. Compression artefacts, noise, and designed linear or non-linear degradations in compressed images. To recover linear and nonlinear distortion from distorted images, image de-blocking techniques are applied. Deblocking techniques accepted by police and vigilance division to improve valuable evidences via ruined pictures of observations. An ordinary encoding and decoding quality characteristic of most digital images is the neighboring pixels that are related and subsequently extracts the unwanted disturbances. Our primary goal is to identify the less correlated representation of the digital photos through our research. There are three steps in our proposed approach, step 1 is the statistical feature removal using curvelet transform, then step 2 is the down-sampling of the image with blocking effect artefacts, finally step 3 is the usage of Quality Defined Convolutional Neural Network (QD-CNN) for the removal of blocking effect artefacts then finally the performance validation is carried out using quality assessment.

关键词： Image quality Ultrasonic transducers Image coding Transducers Digital images Nonlinear distortion Transforms Reproducibility of results Quality assessment Convolutional neural networks

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A Survey on: Personality Prediction from multimedia through Machine Learning 5

A Survey on: Personality Prediction from Multimedia through ...

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5th International conference on computing Methodologies and Communication, ICCMC 2021

作者： Singh, Brijesh Kumar Katiyar, Mansi Gupta, Shefali Ganpatrao, Nikam Gitanjali National Institute of Technology Department of Computer Applications Kurukshetra Haryana India

ISBN: (纸本)9781665403603

The prediction of the personality of an individual is a critical problem in both areas whether it is considered in the context of organizations or in the case of our daily lives. Prediction of personality depends on many factors and these factors may vary from one individual to *** prediction is identifying the personalities of individuals through their actions in different situations and observing their behaviours in various circumstances. Personality traits show the different characteristics of different people based on their thoughts, feelings, and behaviours. There can be positive as well as negative personality traits. Personality traits are based on the Big Five Model also known as the OCEAN model i.e. Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. In the previous study, many investigations has been done. They have used different techniques and different algorithms to predict the personality of different people. Some have used handwriting to predict personality using the GSC algorithm. Facial expressions have been used in some studies using CNN features. Few studies have focused on the social networking sites for personality prediction by examining an individual's reaction to different posts, their comments, their posts, etc. One study predicted personality using AU, LF, POS, Emotional features and their combinations. Apart from this, there are few limitations in these single models discussed above. They work efficiently for only a small dataset but on increasing the size of the dataset their accuracy keeps decreasing. Multimodal is effective in this case and to make the task automatic, an intelligent multimodal agent can identify personality traits better based on both verbal and non-verbal features. © 2021 IEEE.

关键词： Forecasting

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Markerless Augmented Reality Application for Interior Designing

Markerless Augmented Reality Application for Interior Design...

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Advanced Technologies in Intelligent Control, Environment, computing & Communication Engineering (ICATIECE), International conference on

作者： Nagashree R R Rakshitha M Ruchira Bharadwaj S Shabana Sultana Narasimha Mahesh Nadig Department of Computer Science The National Institute of Engineering Mysuru India The National Institute of Engineering Mysuru India

ISBN: (纸本)9781665493970

Markerless Augmented Reality - by using the screen of your smartphone, tablet, or AR glasses, you may place and examine digital things in the real world without the use of a marker like a QR Code, location, image, etc. Pokémon GO was one of the games that made AR a popular topic. Since then, several AR-enabled goods have been created, including social networking app filters, virtual footwear, sunglasses, and more. Often, we need to download applications such as Instagram or Facebook to view content. The introduction of web-based augmented reality implies that downloading a data-intensive and compute-intensive app is longer needed. Today, augmented reality material may be viewed through a browser. This web-based AR methodology is a new potential, due to its low bandwidth, accessibility, and speed. The advantages of WebAR are double. First, the restriction is too lax for viewers to access and share your content. People are more likely to browse and share content when an app is absent. This guarantees greater accessibility, increased connectedness, and a greater sharing feature. Because there is no application, there is minimal expense and flexibility, which leads to the second benefit. You can link to ongoing initiatives, for instance, by pushing fresh material quicker and more affordably. With our software, users may examine a specified 3D model, which is a representation of visual furniture without markers and can be viewed and altered in real-time. This is a step toward Web-based AR. This study suggests a novel method for applying augmented reality technology to furniture. Using a versatile online interface on a variety of devices, the user can connect 3D virtual furniture data and observe visual furniture.

关键词： Visualization Solid modeling Three-dimensional displays Social networking (online) multimedia Web sites QR codes Information filters

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2020 16th International conference on Wireless and Mobile computing, networking and Communications, WiMob 2020

2020 16th International Conference on Wireless and Mobile Co...

引用

16th International conference on Wireless and Mobile computing, networking and Communications, WiMob 2020

ISBN: (纸本)9781728197227

The proceedings contain 66 papers. The topics discussed include: medium access probability model based on CSMA/CA for a DSRC network driven by Poisson line process;learning congestion over millimeter-wave channels;a Q-learning approach for the support of reliable transmission in the Internet of underwater things;machine learning-based methods for path loss prediction in urban environment for LTE networks;towards the complexity of the widest path problem in hybrid multi-channel WMNs;a color adjustment method for HDR display of video content received over wireless multimedia networks;characterization of the indoor-to-outdoor wireless channel in air-to-ground communication systems;and improving health information exchange through wireless communication protocols.

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Accessibility of Social Media Application for Blinds

Accessibility of Social Media Application for Blinds

引用

computing & Information Sciences (ICCIS), International conference on

作者： Mamoona Atif Swati Tahir Mustafa Madni Uzair Iqbal Janjua Iftikhar Ahmad COMSATS University Islamabad Pakistan

ISBN: (纸本)9781665494427

With the increase in technology, social networking became an essential component of human life. People communicate and share information through social networking applications, such as Facebook, Twitter and Instagram. However, it is observed that accessing such applications for visually impaired or blind users is challenging and such applications are reported as highly inaccessible. This study aims to analyze the usefulness of Facebook Messenger application for blind users using smartphone devices. Initially, an experiment is conducted with five blind people and their interaction with existing Facebook Messenger application is observed. Finally, the future work is suggested based on the obtained findings in the design of new social networking application. Further it will be compared with available applications to enhance the usability for blind users.

关键词： Social networking (online) Navigation multimedia Web sites Blogs Keyboards Prototypes Object recognition

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Comparison of Machine Learning Methods in Sentiment Analysis PeduliLindungi Applications

Comparison of Machine Learning Methods in Sentiment Analysis...

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International conference on Informatics, multimedia, Cyber and Information System (ICIMCIS)

作者： Widya WH Cholil Febriyanti Panjaitan Fediansyah Ferdiansyah Artika Arista Ria Astriratma Tri Rahayu Faculty of Computer Science Universitas Pembangunan Nasional Veteran Jakarta Jakarta Indonesia Faculty of Engineering Universitas Sriwijaya Faculty of Computer Science Universitas Bina Darma Palembang Indonesia Faculty of Computing Universiti Teknologi Malaysia

ISBN: (纸本)9781665473286

KOMINFO, in partnership with the Ministry of State-Owned Enterprises (BUMN), developed the “PeduliLindungi” smartphone application, which has been implemented in the community. This application is unquestionably a topic that is frequently debated by the community via social media, namely Twitter. This PeduliLindung program harvests the positives and negatives of Twitter public opinion. Through posting on Twitter and expressing opinions on this problem, data can be collected and evaluated into negative and favorable opinion sentiments. From this occurrence, study was undertaken to analyze Twitter users' perspectives on the application's security. A dataset including 1,922 tweets will be analyzed using five Machine Learning techniques: Naive Base, SVM, Decision Tree (DT), K-Nearest Neighbor (KNN), and Random Forest. Then, it will be compared to the value of accuracy and AUC score for the performance of each of the five approaches. Collecting user tweet data with crawling techniques, data processing, word weighting with the TD-IDF approach, modeling with five machine learning methods, comparison, and algorithm selection are the stages of the suggested study method. The results indicated that the accuracy of the KNN method was 98.40%, Decision Tree 97.60%, Random Forest 97.60%, SVM 97.10%, and Naive Bayes 87.70%, while the AUC score for each method was 99.80% for KNN, 99.80% for Decision Tree, 99.80% for Random Forest, 95.80% for SVM, and 91.27% for Naive Bayes. With an accuracy value of 98.40% and an AUC Score of 98.80%, the KNN approach gives superior performance than the other methods.

关键词： Support vector machines Sentiment analysis Machine learning algorithms Social networking (online) multimedia systems Blogs Data processing

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Managing multimedia Big Data: Security and Privacy Perspective 1

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4th International conference on ICT for Sustainable Development, ICT4SD 2019

作者： Kapil, Gayatri Ishrat, Zaiba Kumar, Rajeev Agrawal, Alka Khan, Raees Ahmad Lucknow India Department of Electronics and Communication IIMT College of Engineering Greater Noida India

ISBN: (数字)9789811509360

ISBN: (纸本)9789811509353

multimedia Big Data (MMBD) is growing day by day because data is created by everyone and for everything from mobile devices, digital camera, digital game, multimedia sensor, video lectures, and social networking sites, etc. It is observed that various organizations and businesses moving toward it and taking out the benefits from it. MMBD has its applications in various fields that containing social networking sites, science and research, business enterprises, surveillance image and video, health care industries, etc. Huge size of data is being transfered from the Internet in each second. Due to increase in MMBD, it is necessary to protect this data from external threats or malicious attacks which can affect privacy, integrity, and genuineness of information systems. Therefore, to protect the integrity, identity, and security of individual’s data from these unwanted malicious attacks, an effective security mechanism is much needed. In this paper, the author discussed about the current issues in MMBD and security approaches to deal with these issues and also, focus on multimedia big data security and privacy. It also enumerated the directions to be taken while using MMBD along with security measures. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Cryptography

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Deep Regression Model for Videos Popularity Prediction in Mobile Edge Caching Networks

Deep Regression Model for Videos Popularity Prediction in Mo...

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International conference on Information networking

作者： Arooj Masood The-Vi Nguyen Sungrae Cho School of Computer Science and Engineering Chung-Ang University Seoul Republic of Korea

ISBN: (数字)9781728191010

ISBN: (纸本)9781728191027

In recent years, the wide spread adoption of mobile and multimedia applications has resulted in exponentially increasing multimedia traffic, which exerts a great burden on backhaul links and mobile core networks. Mobile edge computing (MEC) alleviates the problem by enabling mobile edge devices with cache storage and allowing them to store popular multimedia contents requested by users to reduce network congestion and content delivery latency. However, to decide the multimedia contents to cache in the edge devices, the popularity of contents needs to be taken into consideration. In this paper, we propose a deep regression-based video popularity estimation for proactive video caching in MEC networks. In each time slot, an edge device, i.e., base station (BS) generates local estimates on content popularity, which are then shared by the neighboring edge devices. Then, each edge device performs popularity prediction using a deep regression technique for proactive content caching for the next time slot. Simulation results show that the proposed deep regression based method for videos popularity prediction achieves good performance and reduces latency significantly.

关键词： Performance evaluation Simulation Neural networks Estimation multimedia computing Streaming media Predictive models

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