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An Efficient Detection Approach of Content Aware Image Resizing

computers, Materials & Continua 2020年第8期64卷 887-907页

作者： Ming Lu Shaozhang Niu Zhenguang Gao Beijing Key Lab of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and TelecommunicationsBeijing100876China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan114051China Department of Computer Science Framingham State UniversityMassachusettsMA 01772USA

Content aware image resizing(CAIR)is an excellent technology used widely for image *** can also be used to tamper with images and bring the trust crisis of image content to the *** an image is processed by CAIR,the correlation of local neighborhood pixels will be *** local binary patterns(LBP)can effectively describe the local texture,it however cannot describe the magnitude information of local neighborhood pixels and is also vulnerable to ***,to deal with the detection of CAIR,a novel forensic method based on improved local ternary patterns(ILTP)feature and gradient energy feature(GEF)is proposed in this ***,the adaptive threshold of the original local ternary patterns(LTP)operator is improved,and the ILTP operator is used to describe the change of correlation among local neighborhood pixels caused by ***,the histogram features of ILTP and the gradient energy features are extracted from the candidate image for CAIR forgery ***,the ILTP features and the gradient energy features are concatenated into the combined features,and the combined features are used to train *** support vector machine(SVM)is exploited as a classifier to be trained and tested by the above features in order to distinguish whether an image is subjected to CAIR or *** candidate images are extracted from uncompressed color image database(UCID),then the training and testing sets are *** experimental results with many test images show that the proposed method can detect CAIR tampering effectively,and that its performance is improved compared with other *** can achieve a better performance than the state-of-the-art approaches.

关键词： Digital image forensics content aware image resizing local ternary patterns gradient energy feature

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CMG-Net: An End-to-End Contact-based Multi-Finger Dexterous Grasping Network

CMG-Net: An End-to-End Contact-based Multi-Finger Dexterous ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Mingze Wei Yaomin Huang Zhiyuan Xu Ning Liu Zhengping Che Xinyu Zhang Chaomin Shen Feifei Feng Chun Shan Jian Tang School of Software Engineering East China Normal University China School of Computer Science East China Normal University China Midea Group China School of Electronics and Information Guangdong Polytechnic Normal University China

In this paper, we propose a novel representation for grasping using contacts between multi-finger robotic hands and objects to be manipulated. This representation significantly reduces the prediction dimensions and accelerates the learning process. We present an effective end-to-end network, CMG-Net, for grasping unknown objects in a cluttered environment by efficiently predicting multi-finger grasp poses and hand configurations from a single-shot point cloud. Moreover, we create a synthetic grasp dataset that consists of five thousand cluttered scenes, 80 object categories, and 20 million annotations. We perform a comprehensive empirical study and demonstrate the effectiveness of our grasping representation and CMG-Net. Our work significantly outperforms the state-of-the-art for three-finger robotic hands. We also demonstrate that the model trained using synthetic data perform very well for real robots.

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A Robust Anonymous Authentication Scheme for Securing IoT-Enabled Healthcare Services

A Robust Anonymous Authentication Scheme for Securing IoT-En...

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Cyber Security in Networking Conference (CSNet)

作者： Sana Ullah Shanshan Tu Akhtar Badshah Muhammad Waqas Department of Enterprise and Technology Atlantic Technological University Galway Ireland College of Computer Science Beijing University of Technology Beijing China Department of Software Engineering Faculty of Engineering University of Malakand Dir Lower Pakistan Faculty of Engineering and Science School of Computing and Mathematical Sciences University of Greenwich U.K. School of Engineering Edith Cowan University Perth WA Australia

ISBN: (数字)9798331534103

ISBN: (纸本)9798331534110

Smart wearables and body implanted IoT devices continuously track and transmit health metrics wirelessly to a central controller, such as a personal server, enabling real-time monitoring, proactive treatment and timely assistance. However, the security of IoT-enabled healthcare systems remains a concern, particularly regarding the protection of sensitive patient data. To address the aforementioned issue and enhance security in IoT-enabled healthcare services, we propose a robust anonymous authentication scheme in this work. By leveraging lightweight cryptographic primitives including hash functions and XOR operations coupled with physical unclonable functions (PUFs) and fuzzy extractors, the proposed scheme introduces a novel authentication and key agreement mechanism for secure communication in IoT-enabled healthcare systems. The integration of PUF technology ensure the physical security of resource constrained implantable medical devices (IMDs) against various attacks including device physical capture and impersonation attacks that compromise the IoT-enabled healthcare operations. A thorough security analysis demonstrated the robustness and resilience of the proposed scheme against various active and pas-sive security attacks, thus ensure the integrity and confidentiality of sensitive healthcare data. Finally, comparative analysis of the proposed scheme with other state-of-the-art highlighted that our scheme outperformed other approaches in terms of providing security and additional features, positioning it as a comprehensive solution for authentication in IoT-enabled healthcare services.

关键词： Hash functions Medical devices Authentication Medical services Physical unclonable function Security Internet of Things Servers Wearable devices Resilience

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CMG-Net: An End-to-End Contact-Based Multi-Finger Dexterous Grasping Network

arXiv

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arXiv 2023年

作者： Wei, Mingze Huang, Yaomin Xu, Zhiyuan Liu, Ning Che, Zhengping Zhang, Xinyu Shen, Chaomin Feng, Feifei Shan, Chun Tang, Jian School of Software Engineering East China Normal University China School of Computer Science East China Normal University China Midea Group China School of Electronics and Information Guangdong Polytechnic Normal University China

关键词： Robotic arms

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Cross-media Scientific Research Achievements Query based on Ranking Learning

arXiv

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arXiv 2022年

作者： Wang, Benzhi Liang, Meiyu Li, Ang School of Computer Science National Pilot School of Software Engineering Beijing University of Posts and Telecommunications Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing100876 China

With the advent of the information age, the scale of data on the Internet is getting larger and larger, and it is full of text, images, videos, and other information. Different from social media data and news data, scientific research achievements information has the characteristics of many proper nouns and strong ambiguity. The traditional single-mode query method based on keywords can no longer meet the needs of scientific researchers and managers of the Ministry of science and Technology. Scientific research project information and scientific research scholar information contain a large amount of valuable scientific research achievement information. Evaluating the output capability of scientific research projects and scientific research teams can effectively assist managers in decision-making. In view of the above background, this paper expounds on the research status from four aspects: characteristic learning of scientific research results, cross-media research results query, ranking learning of scientific research results, and cross-media scientific research achievement query system. Copyright © 2022, The Authors. All rights reserved.

关键词： Decision making

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NOMABER: A Novel Framework for Multi-Type Abnormal Behaviour Recognition

NOMABER: A Novel Framework for Multi-Type Abnormal Behaviour...

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IEEE International Conference on Big Data

作者： Ao Wang Yongchen Yao Yuhan Yao Jinpeng Chen School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing P.R. China

ISBN: (纸本)9781665480468

Recently, using machine learning technology to realize abnormal behavior recognition in video surveillance to replace human monitoring has become a hot academic topic. In that case, constructing an efficient and unified framework for multi-type abnormal behavior recognition is a worthy topic in machine learning research. This research aims to design a lightweight recognition framework that can recognize various abnormal behaviors in real-time. We propose a Novel framewOrk for the Multi-type Abnormal BEhavior Recognition (NOMABER), which consists of three parts. Firstly, the improved image pre-processing module annotates the abnormal behaviors of image data sets. Secondly, the improved YOLOv5 module is used to identify the multi-type abnormal behaviors, and then the abnormal behaviors are classified by the output module. Finally, experiments on real data sets show that NOMABER is superior to the current methods of real-time performance, identification accuracy, and types of abnormal behaviors.

关键词： Training Image recognition Annotations Machine learning Big Data Video surveillance Real-time systems

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Stock market prediction based on machine learning and social sentiment analysis

TechRxiv

引用

TechRxiv 2023年

作者： Ghazanfar, Mustansar Ali Anwar, Madiha Lee, Sin Wee Qazi, Nadeem Karimi, Amin Jhanjhi, N.Z. Javed, Ali School of Architecture Computing and Engineering University of East London United Kingdom Deputy Head of School of Computing Arden University United Kingdom School of Computer Science SCS Taylor’s University Malaysia Department of Software Engineering UET Taxila Pakistan

Precise stock market prediction is crucial for investors, but the volatility of the stock market is influenced by multiple factors such as public sentiments, business news, and related product volatility. While several algorithms have been proposed to predict the stock exchange index based on historical data, they are not ideal as external factors play a critical role in market volatility. To address this issue, we proposed a machine learning model that incorporates historical data with external factors such as social media sentiments, oil and gold trends, and financial news data to enhance prediction accuracy. Our study used HPQ, IBM, ORCL, and MSFT stock market datasets to validate the effectiveness of the proposed model, including an analysis of the impact of Covid19 on companies. Our experimental results showed the highest accuracy of 87.2% using oil and sentiment datasets. Additionally, we identified that social media significantly affects IBM stocks, and the GBM (Gradient Boosting Classifier) classifier produced consistent results. © 2023, CC BY.

关键词： Sentiment analysis

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Dimensionality Reduction for General KDE Mode Finding

arXiv

引用

arXiv 2023年

作者： Luo, Xinyu Musco, Christopher Widdershoven, Cas Department of Computer Science Purdue University Indiana United States Tandon School of Engineering New York University New York United States State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

Finding the mode of a high dimensional probability distribution D is a fundamental algorithmic problem in statistics and data analysis. There has been particular interest in efficient methods for solving the problem when D is represented as a mixture model or kernel density estimate, although few algorithmic results with worst-case approximation and runtime guarantees are known. In this work, we significantly generalize a result of (Lee et al., 2021) on mode approximation for Gaussian mixture models. We develop randomized dimensionality reduction methods for mixtures involving a broader class of kernels, including the popular logistic, sigmoid, and generalized Gaussian kernels. As in Lee et al.’s work, our dimensionality reduction results yield quasi-polynomial algorithms for mode finding with multiplicative accuracy (1 − ϵ) for any ϵ > 0. Moreover, when combined with gradient descent, they yield efficient practical heuristics for the problem. In addition to our positive results, we prove a hardness result for box kernels, showing that there is no polynomial time algorithm for finding the mode of a kernel density estimate, unless P = NP. Obtaining similar hardness results for kernels used in practice (like Gaussian or logistic kernels) is an interesting future direction. Copyright © 2023, The Authors. All rights reserved.

关键词： Gradient methods

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An AI-Human Collaboration Chatbot with Digital Representation of Users for Knowledge Sharing

An AI-Human Collaboration Chatbot with Digital Representatio...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Sruthy Anand Adhwaith B Pillai A M Abhishek Sai Sethuraman N Rao Center for Wireless Networks & Applications (WNA) Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Software Development Engineer Fridayy AI Noida India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

With the advancement in Artificial Intelligence (AI), especially generative AI, conversational agents are becoming a norm in everyday lives. These agents can become more intelligent if they can collaborate with humans or other agents in analysing the conversation with the user. This work presents the use of AI to connect different sets of users and help them solve their life problems. Currently, sharing the wisdom of the old generation is happening within the close family or the student circle alone. To receive knowledge from a wider community at their fingertips, we designed a chatbot called wisdomBOT which can connect the younger generation users with the older generation users to receive their wisdom through the sharing of life experiences and stories and to learn from them. It also helps to address the loneliness of the older generation and connect them through a new way to make younger friends.

关键词： Pervasive computing Sentiment analysis Social networking (online) Generative AI Digital representation Collaboration Oral communication Chatbots Security Older adults

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Incomplete Multi-View Representation Learning Through Anchor Graph-Based GCN and Information Bottleneck

Incomplete Multi-View Representation Learning Through Anchor...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhenjiao Liu Xiao Wang Xiaodi Huang Guanlin Li Ke Sun Zhikui Chen School of Software Technology Dalian University of Technology Dalian China Samovar Telecom SudParis Institut Polytechnique de Paris Palaiseau France School of Computing Mathematics and Engineering Charles Sturt University Albury Australia School of Computer Science and Technology Dalian University of Technology

Real-world data often contain incomplete views with varying degrees of missing information. While there are existing methods for learning representations from such data, effectively utilizing all incomplete view data and ensuring robustness to different levels of completeness remains a challenging task. To address this problem, we propose a novel framework named IMRL-AGI. IMRL-AGI combines the anchor graph-based Graph Convolutional Network (GCN) and information bottleneck. Specifically, the framework starts by constructing an anchor graph to effectively captures the nonlinear information between instances. Next, an anchor graph-based GCN is designed to extract feature information from various views. IMRL-AGI maximizes the mutual information between the views obtained by the common representation and the anchor-graph-based GCN, ensuring the accurate extraction of view information. Furthermore, the minimization of mutual information is applied to promote diversity and reduce redundancy in the multi-view representation. Extensive experiments are conducted on several real-world datasets, and the results demonstrate the superiority of IMRL-AGI.

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