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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Ramiz Salama Fadi Al-Turjman Manisha Aeri Satya Prakash Yadav Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Mersin Turkey Artificial Intelligence Engineering Dept. AI and Robotics Institute Near East University Nicosia Mersin Turkey Research Center for AI and IoT Faculty of Engineering University of Kyrenia Kyrenia Mersin Turkey Computer Science and Engineering Graphic Era Hill University Dehradun Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management (GLBITM) Greater Noida India

The basic goal of the IoT is to permit the online connection and communication of everyday physical objects like appliances, vehicles, and other gadgets. This paves the way for effortless and automated data collection and exchange, as well as interaction between the objects themselves and with humans. The Internet of Everything (IoE) is an expansion of the Internet of Things (IoT) that aims to maximize and improve human life by connecting and making the most of all available resources (humans, information, and technology). The transportation, healthcare, agricultural, energy, and manufacturing sectors are all examples. The IoIT aims to incorporate AI and ML within the IoT’s interconnected network of physical items. These objects can then independently behave in response to the information they have gathered and the instructions they have been given. This has the potential to lead to smarter, more efficient systems that can react to new information and human input in real time. The Internet of Things, the Internet of Everything, and the Industrial Internet of Things all have the same overarching goal: to pave the way for the development of more efficient and intelligent systems and processes that can enhance a wide range of human endeavors. However, in order to make sure that these technologies are used in a responsible and ethical way, it will be necessary to carefully analyze their consequences and the issues they provide.

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Intelligent Hardware Solutions for COVID -19 and Alike Diagnosis - A survey

Intelligent Hardware Solutions for COVID -19 and Alike Diagn...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

Globally, 210 nations have been affected by the 2019 Novel Coronavirus (COVID-19), which has been classified as a pandemic. The modern health system, as well as the economic, educational, and social sides of society, have all been severely impacted. While the rate of transmission keeps increasing, several cooperative strategies between stakeholders to create cutting-edge methods of screening and detecting COVID-19 instances among people at a comparable rate have been noticed. Also, the importance of computational models connected to the technologies of the fourth industrial revolution in accomplishing the desired feat has been emphasized. Unfortunately, there is a gap in the precision of COVID-19 case detection, prediction, and contact tracking. For patients with COVID-19 in isolation units, teleultrasound (TUS), particularly with the support of fifth generation (5G) wireless transmission technology, can offer rapid monitoring, quick clinical progress assessment, and assistance with guiding interventional procedures. Also, it helps conserve medical resources like equipment and supplies while lowering the risk of infection among medical personnel. The review of computer models presented in this work can be used to improve the effectiveness of COVID-19 pandemic case detection and prediction. We concentrate on adoptable big data, AI, and nature-inspired computing solutions for the current pandemic. According to the review, models inspired by nature have shown strong performance in feature selection for medical problems. In pandemic-related cases like COVID-19, contact tracing using big data analytics should also be investigated.

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(Benefits of Internet of Things (IoT) Applications in Health care - An Overview)

(Benefits of Internet of Things (IoT) Applications in Health...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Ramiz Salama Fadi Al-Turjman Preeti Chaudhary Satya Prakash Yadav Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Mersin Turkey Artificial Intelligence Engineering Dept. AI and Robotics Institute Near East University Nicosia Mersin Turkey Research Center for AI and IoT Faculty ofEngineering University of Kyrenia Kyrenia Mersin Turkey Computer Science and Engineering Graphic Era Hill University Dehradun Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management (GLBITM) Greater Noida India

In a nutshell, the IoT refers to the idea of integrating high-tech technology in the form of "smart" gadgets and sensors into a single setting to gather data. There are still numerous areas in which machines cannot compete favorably with people, but data collection and analysis are undeniably two of their strongest points. The Internet of Things makes it possible to fully or partially automation data, it involves related activities, which is obviously very important for the healthcare industry. What exactly is the Internet connected medical things, and how exactly might it make a significant impact on the sector? By having to apply the concept of the internet of things (IoT) to the field of medicine, several significant advancements have become possible, including the ability to automatically control the thermostat of containers while transmitting inoculations and antibiotics, the accurate tracking of the ailments of a lot of illnesses without a need for additional meetings with a general practitioner, the correct and most useful use of meds, and a great deal more. All of this is realizable because of the capability of MedTech gadgets to carry out continuous monitoring system. The potential applications of internet of things technology in healthcare are very interesting. Let’s review the ways in which this technology.

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Robust Co-Design of Canonical Underactuated Systems for Increased Certifiable Stability

Robust Co-Design of Canonical Underactuated Systems for Incr...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Federico Girlanda Lasse Shala Shivesh Kumar Frank Kirchner Robotics Innovation Center German Research Center for Artificial Intelligence (DFKI GmbH) Bremen Germany Department of Information Engineering University of Padova Italy Department of Mechanics & Maritime Sciences Dynamics Division Chalmers University of Technology Gotheburg Sweden Faculty of Mathematics and Computer Science University of Bremen Bremen Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Optimal behaviours of a system to perform a specific task can be achieved by leveraging the coupling between trajectory optimization, stabilization, and design optimization. This approach is particularly advantageous for underactuated systems, which are systems that have fewer actuators than degrees of freedom and thus require for more elaborate control systems. This paper proposes a novel co-design algorithm, namely Robust Trajectory Control with Design optimization (RTC-D). An inner optimization layer (RTC) simultaneously performs direct transcription (DIRTRAN) to find a nominal trajectory while computing optimal hyperparameters for a stabilizing time-varying linear quadratic regulator (TVLQR). RTC-D augments RTC with a design optimization layer, maximizing the system’s robustness through a time-varying Lyapunov-based region of attraction (ROA) analysis. This analysis provides a formal guarantee of stability for a set of off-nominal states. The proposed algorithm has been tested on two different underactuated systems: the torque-limited simple pendulum and the cart-pole. Extensive simulations of off-nominal initial conditions demonstrate improved robustness, while real-system experiments show increased insensitivity to torque disturbances.

关键词： Couplings Torque Regulators Stability analysis Robustness Task analysis robotics and automation

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Regress Before Construct: Regress Autoencoder for Point Cloud Self-supervised Learning

arXiv

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arXiv 2023年

作者： Liu, Yang Chen, Chen Wang, Can King, Xulin Liu, Mengyuan College of Computer Science Sichuan University China Center for Research in Computer Vision University of Central Florida United States Laboratory on Multimedia Information Processing The Department of Computer Science Kiel University Hangzhou Linxrobot Company China Hangzhou GOTHEN Technology Co. Ltd China Key Laboratory of Machine Perception Shenzhen Graduate School Peking University China

Masked Autoencoders (MAE) have demonstrated promising performance in self-supervised learning for both 2D and 3D computer vision. Nevertheless, existing MAE-based methods still have certain drawbacks. Firstly, the functional decoupling between the encoder and decoder is incomplete, which limits the encoder’s representation learning ability. Secondly, downstream tasks solely utilize the encoder, failing to fully leverage the knowledge acquired through the encoder-decoder architecture in the pre-text task. In this paper, we propose Point Regress AutoEncoder (Point-RAE), a new scheme for regressive autoencoders for point cloud self-supervised learning. The proposed method decouples functions between the decoder and the encoder by introducing a mask regressor, which predicts the masked patch representation from the visible patch representation encoded by the encoder and the decoder reconstructs the target from the predicted masked patch representation. By doing so, we minimize the impact of decoder updates on the representation space of the encoder. Moreover, we introduce an alignment constraint to ensure that the representations for masked patches, predicted from the encoded representations of visible patches, are aligned with the masked patch presentations computed from the encoder. To make full use of the knowledge learned in the pre-training stage, we design a new finetune mode for the proposed Point-RAE. Extensive experiments demonstrate that our approach is efficient during pre-training and generalizes well on various downstream tasks. Specifically, our pretrained models achieve a high accuracy of 90.28% on the ScanObjectNN hardest split and 94.1% accuracy on ModelNet40, surpassing all the other self-supervised learning methods. Our code and pretrained model are public available at: https://***/liuyyy111/ Point-RAE. Copyright © 2023, The Authors. All rights reserved.

关键词： Supervised learning

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Authentication using Biometric Data from Mobile Cloud Computing in Smart Cities

Authentication using Biometric Data from Mobile Cloud Comput...

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Advancement in Electronics & Communication Engineering (AECE), International Conference on

作者： Ramiz Salama Sinem Al-Turjman Chadi Altrjman Fadi Al-Turjman R.Om Prakash Satya Prakash Yadav Satvik Vats Department of Computer Engineering Research Center for AI and IoT AI and Robotics Institute Artificial Intelligence Engineering Dept AI and Robotics Institute Near Turkey Department of Chemical Engineering Waterloo University ON Canada Noida Instue Engineering and Technology Greator Noida U.P India Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and A1anagement(GLBITAM) Greater Noida India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

Smart cities are cities that are designed to be more efficient, sustainable, and connected. As our cities grow and become more complex, it's important to find new ways to address the challenges that arise, such as security, privacy, and mobility. This is where mobile cloud computing and biometric authentication come in. Mobile cloud computing is a way to provide cloud computing services to mobile devices, which means people can access services and data from anywhere and at any time. Biometric authentication is a method of identifying people based on their unique physical or behavioral characteristics, like fingerprints, facial recognition, or voice recognition. These two technologies can be combined to create a secure and personalized environment for residents and visitors in smart cities. By using mobile devices, IoT devices, wireless sensor networks, and edge computing, we can collect real-time data that can be used for big data analytics and machine learning. This means we can optimize city services, like traffic management, waste management, or energy consumption, and enhance the quality of life for people living in smart cities. However, the use of these technologies also poses significant security and privacy risks, so it's important to design systems that are secure and privacy-preserving. This requires interdisciplinary research and collaboration between experts in different fields. In summary, mobile cloud computing and biometric authentication have the potential to transform smart cities by creating a more secure and personalized environment for residents and visitors. But we must also be mindful of the potential risks and work together to address them.

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Heterogeneous Wireless Access Networks for IoT and E-Health-A Survey

Heterogeneous Wireless Access Networks for IoT and E-Health-...

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Advancement in Electronics & Communication Engineering (AECE), International Conference on

作者： Ramiz Salama Chadi Altrjman Fadi Al-Turjman Satya Prakash Yadav Satvik Vats Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT. Near East University Near East Boulevard Nicosia TURKIYE Department of Chemical Engineering Waterloo University ON Canada Artificial Intelligence Engineering Dept AI and Robotics Institute Near East University Nicosia Turkey Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management(GLBITM) Greater Noida India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

In a heterogeneous wireless environment, there are many different radio access technologies (RATs), such as Wi-Fi and 2G, 3G, 4G, and 5G, which each have a different degree of coverage and processing power to meet various service requirements. A mobile user may have access to various access networks in such a situation. On the PC of each user, numerous programs with varied Quality of Service (QoS) requirements can operate simultaneously. It would be advantageous for a multi-interface terminal to use two or more interfaces simultaneously in order to improve performance. However, using multiple networks at once could use up more energy than just using one interface. Therefore, energy use must to be taken into account when analyzing the Flow/Interface Relationship (FIA). This paper presents a novel method for choosing the ideal FIA that achieves the best trade-off between all the characteristics taken into account, dubbed Smart Tabu Search (STS). STS considers user preferences, network circumstances, network expenses, application QoS specifications, and battery life of mobile devices. We use simulations and testbed experiments to validate our concept. Professionals are now able to diagnose and monitor patients remotely because to the growing use of healthcare monitoring devices. One of the most frequent occurrences that affects the reliability of information transmission in any network is congestion, which is defined as the unchecked increase in traffic relative to network capacity. With the use of a mesh network, wireless sensors, and some of the most significant models for vital signs, the article aims to provide a realistic simulation environment for a healthcare system. The simulator environment is a helpful tool for assessing the dependability and effectiveness of the healthcare system in a realistic setting. However, the system is not appropriate for real-time applications due to the sluggish network adaption caused by the end-to-end based traffic regulation deci

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Study on the Impact of Situational Explanations and Prior Information Given to Users on Trust and Perceived Intelligence in Autonomous Driving in a Video-based 2x2 Design

Study on the Impact of Situational Explanations and Prior In...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Kolja Kühnlenz Barbara Kühnlenz Department of Electrical Engineering and Computer Science Robotics Research Lab Coburg University of Applied Sciences and Arts Coburg Germany Academic Center for Sciences and Humanities Coburg University of Applied Sciences and Arts Coburg Germany

In this paper, results from a video-based study on the influence of prior information given to users and explanations situationally given by the vehicle itself on trust and perceived intelligence are presented using a simulated autonomous vehicle in an ambiguous driving situation. A 2x2 between-subjects design is chosen with two independent variables ‘prior information’ (extended/short) and ‘explanations’ (yes/no) with users pseudo-randomly assigned to one of the four conditions. Significant results from 189 test persons reveal, that trust depends on how the capabilities of the intelligent vehicle are explained a priori and not on situational explanations, while perceived intelligence is influenced by both variables. Additional interactions of prior information and user gender is noted with respect to perceived intelligence. As one side effect, it is found, that male users felt significantly more safe than female users with also higher ratings of intention to use the vehicle independently of given information and explanations. Another side effect is that situational explanations lead to better ratings of subjective performance, while also here a significant interaction of gender and prior information is noted. Thus, contrary to expectations, a dominant role of continuous situational explanations (Explainable AI) of the intelligent vehicle for increasing trust is not confirmed and the extent of given prior information seems the deciding factor for initial trust building, which is an important aspect for the introduction of new intelligent technology into society. This is remarkable as at the same time perceived intelligence seems to be dependent on both variables. So it appears, that a vehicle being able to explain its actions may well appear more intelligent, but not necessarily appear more trustworthy.

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TransXNet: Learning Both Global and Local Dynamics with a Dual Dynamic Token Mixer for Visual Recognition

arXiv

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arXiv 2023年

作者： Lou, Meng Zhou, Hong-Yu Yang, Sibei Yu, Yizhou The Artificial Intelligence Laboratory Deepwise Healthcare Beijing China The Department of Computer Science The University of Hong Kong Hong Kong The Shanghai Engineering Research Center of Intelligent Vision and Imaging ShanghaiTech University Shanghai China

Recent studies have integrated convolution into transformers to introduce inductive bias and improve generalization performance. However, the static nature of conventional convolution prevents it from dynamically adapting to input variations, resulting in a representation discrepancy between convolution and self-attention as self-attention calculates attention matrices dynamically. Furthermore, when stacking token mixers that consist of convolution and self-attention to form a deep network, the static nature of convolution hinders the fusion of features previously generated by self-attention into convolution kernels. These two limitations result in a suboptimal representation capacity of the constructed networks. To find a solution, we propose a lightweight Dual Dynamic Token Mixer (D-Mixer) that aggregates global information and local details in an input-dependent way. D-Mixer works by applying an efficient global attention module and an input-dependent depthwise convolution separately on evenly split feature segments, endowing the network with strong inductive bias and an enlarged effective receptive field. We use D-Mixer as the basic building block to design TransXNet, a novel hybrid CNN-Transformer vision backbone network that delivers compelling performance. In the ImageNet-1K image classification task, TransXNet-T surpasses Swin-T by 0.3% in top-1 accuracy while requiring less than half of the computational cost. Furthermore, TransXNet-S and TransXNet-B exhibit excellent model scalability, achieving top-1 accuracy of 83.8% and 84.6% respectively, with reasonable computational costs. Additionally, our proposed network architecture demonstrates strong generalization capabilities in various dense prediction tasks, outperforming other state-of-the-art networks while having lower computational costs. Our code will be available at https://***/LMMMEng/TransXNet. © 2023, CC BY.

关键词： Convolution

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Augmented Box Replay: Overcoming Foreground Shift for Incremental Object Detection

arXiv

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arXiv 2023年

作者： Liu, Yuyang Cong, Yang Goswami, Dipam Liu, Xialei van de Weijer, Joost State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China University of Chinese Academy of Sciences China South China University of Technology China Computer Vision Center Barcelona Spain VCIP CS Nankai University China Department of Computer Science Universitat Autònoma de Barcelona Spain

In incremental learning, replaying stored samples from previous tasks together with current task samples is one of the most efficient approaches to address catastrophic forgetting. However, unlike incremental classification, image replay has not been successfully applied to incremental object detection (IOD). In this paper, we identify the overlooked problem of foreground shift as the main reason for this. Foreground shift only occurs when replaying images of previous tasks and refers to the fact that their background might contain foreground objects of the current task. To overcome this problem, a novel and efficient Augmented Box Replay (ABR) method is developed that only stores and replays foreground objects and thereby circumvents the foreground shift problem. In addition, we propose an innovative Attentive RoI Distillation loss that uses spatial attention from region-of-interest (RoI) features to constrain current model to focus on the most important information from old model. ABR significantly reduces forgetting of previous classes while maintaining high plasticity in current classes. Moreover, it considerably reduces the storage requirements when compared to standard image replay. Comprehensive experiments on Pascal-VOC and COCO datasets support the state-of-the-art performance of our model 1 © 2023, CC BY.

关键词： Object detection

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