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An Inclusive Approach to Addressing Challenges for the Speech and Hearing Impaired 8th

An Inclusive Approach to Addressing Challenges for the Speec...

引用

8th Smart Trends in Computing and Communications (SmartCom)

作者： Sinha, Arohee Kumar, Tarun PES Univ Bengaluru 560085 Karnataka India Indian Inst Sci Ctr Prod Design & Mfg Bengaluru 560012 India

ISBN: (纸本)9789819713226;9789819713233

This paper investigates the prominent communication obstacles faced by individuals affected by speech and hearing impairments, significantly hindering their social interactions, access to information, and daily functionality. In response to the limitations of conventional communication approaches, an exploration of technological advancements becomes imperative. With a focus on augmenting communication for this demographic, the paper underscores the development of applications facilitating speech-to-text and text-to-speech capabilities. By examining existing research and identifying gaps, the paper proposes a comprehensive approach that integrates advanced algorithms, machine learning techniques, and user-centric design principles. The objective is to overcome limitations in current applications, addressing issues like restricted expressive communication and the absence of visual cues. As a model of this approach, the CommunicAID application, coupled with the AUdio product, achieves an accuracy rate of 93% in speech-to-text and text-to-speech conversions. Rigorous user testing among individuals with speech and hearing impairments confirms the app's credibility, substantiating its efficacy in tackling communication challenges and fostering inclusivity for this specific target group.

关键词： Speech impairment Hearing impairment Visual cues Speech-to-text Text-to-speech Communication challenges

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Modeling and Capturing Malicious Behavior to Detect Android Malware 9th

Modeling and Capturing Malicious Behavior to Detect Android ...

引用

9th International Congress on Information and Communication Technology (ICICT)

作者： El AassaL, Ayman Huang, Shou-Hsuan Stephen Univ Houston Dept Comp Sci Houston TX 77204 USA

ISBN: (纸本)9789819735556;9789819735563

The proliferation of Android devices worldwide has fostered an environment conducive to hackers, compelling them to direct their efforts toward Android mobile devices by deploying intricate and sophisticated malware. The malicious actors skillfully employ obfuscation techniques such as polymorphism and dynamic code loading to circumvent conventional malware detection mechanisms. In response to this challenge, our research presents a comprehensive framework to identify Android malware and categorize it into different families based on its run-time behaviors. Our proposed approach starts with parsing system call logs extracted from dynamic analyses of applications. This parsing facilitates the creation of Behavioral Call Graphs (BCGs) through an innovative methodology that accentuates the interrelationships among API calls. Leveraging representation learning algorithms, we represent the inherent characteristics embedded within these BCGs into feature vectors, eliminating the need for manual feature extraction. This work illustrates the efficacy of the framework in achieving highly accurate results in detecting malware and identifying compromised applications.

关键词： Malware detection Malware classification Behavioral call graph Representation learning Deep learning Android system

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U-Net Based Deep Regression Network Architecture for Single Image Super Resolution of License Plate Image 8th

U-Net Based Deep Regression Network Architecture for Single ...

引用

8th Smart Trends in Computing and Communications (SmartCom)

作者： Karthick, S. Muthukumaran, N. Anna Univ Chennai 600025 Tamil Nadu India Sri Eshwar Coll Engn Ctr Computat Imaging & Machine Vis Dept ECE Coimbatore 641202 Tamil Nadu India

ISBN: (纸本)9789819713226;9789819713233

License plate is automatically recognition for various purpose by private or government organization in the universe. As a result of the license plate becoming dull with age, low-quality images are captured for identification. As a result, a deep learning architecture for the high-resolution license plate images was given in this study. Converting low-quality images into high-quality images with the required edge components, including textural information, was the objective of Single Image Super Resolution (SISR). Extra information from the HR pictures is available for use in security, medical imaging, and other applications. It is difficult to get greater precision with less error, because the reconstructed image lacks some information in certain areas. In order to overcome these difficulties, LR pictures are transformed into HR images using a regression network-based super resolution (RNSR). A 50-layer deep regression network is constructed for the SISR. Simulation analysis shows that the proposed RNSR approach converts LR multimedia text images to HR images with 98% accuracy, 94% specificity, and 97% precision. Regression network output can provide images of excellent quality based on the efficiency provided by the proposed RNSR methodology.

关键词： Multimedia text Super resolution image Single Image Super Resolution Deep regression network Low-resolution to high-resolution image

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A Rule-Based Expert System for Automated Document Editing 1

引用

20th International Conference on Computing and Information Technology (IC2IT2)

作者： Varma, Sandeep Shivam, Shivam Roy, Soumya Deep Ray, Biswarup ZS Associates Pune Pune Maharashtra India

ISBN: (数字)9783031585616

ISBN: (纸本)9783031585609;9783031585616

This research paper introduces a Rule-Based Expert System designed for the automated editing of documents in PDF and PPT formats. The system employs a set of predefined rules, extracted from guideline documents using a Large Language Model (LLM), to execute tasks such as redaction of sensitive text/logo detection and annotation of text elements that deviate from prescribed font size guidelines. Following the detection and annotation process, the system further enhances documents by resizing the detected text elements based on the predefined rules. To achieve these editing tasks, the system integrates advanced image processing techniques, leveraging fine-tuned Optical Character Recognition (OCR) for accurate text extraction from document images. Furthermore, Natural Language Processing (NLP) algorithms are utilized to analyze and interpret textual content. The combination of image processing, OCR, NLP, and rule extraction using LLM ensures a comprehensive approach to document editing, enhancing efficiency and accuracy. The proposed system addresses the need for automated and rule-driven document editing, contributing to advancements in information security and document standardization.

关键词： Rule-based Expert System Image processing Optical Character Recognition Document Sanitization Natural Language Processing

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RSV-SLAM: Toward Real-Time Semantic Visual SLAM in Indoor Dynamic Environments

RSV-SLAM: Toward Real-Time Semantic Visual SLAM in Indoor Dy...

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Intelligent systems Conference (IntelliSys)

作者： Habibpour, Mobin Nemati, Alireza Meghdari, Ali Taheri, Alireza Nazari, Shima Sharif Univ Technol Social & Cognit Robot Lab Tehran *** Iran Univ Calif Davis Dept Mech & Aerosp Engn Davis CA 95618 USA

ISBN: (纸本)9783031477232;9783031477249

Simultaneous Localization and Mapping (SLAM) plays an important role in many robotics fields, including social robots. Majority of the existing visual SLAM methods rely on a static world assumption and fail in dynamic environments. In this paper, we proposed a real-time semantic RGB-D SLAM system for dynamic environments that is capable of detecting moving objects and maintaining a static map for robust camera tracking. The proposed model is to eliminate the influence of dynamic objects by introducing deep learning-based semantic information to SLAM systems. Furthermore, we augment the semantic segmentation process using an Extended Kalman filter module to detect dynamic objects that are temporarily idle. We have also implemented a generative network to fill in the missing regions of input images belonging to dynamic objects. This highly modular framework has been implemented on the ROS platform and can achieve around 22 fps on a GTX1080. Benchmarking the developed pipeline on dynamic sequences from the TUM dataset suggests that the proposed approach achieves competitive localization error compared to the state-of-the-art methods while operating in near real-time. The source code is publicly available at https://***/mobiiin/rsv_slam.

关键词： Visual Simultaneous Localization and Mapping Semantic Segmentation Mobile Robot Dynamic Environment

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SLOT: A Soft-Legged Omnidirectional Tetrapod 27th

SLOT: A Soft-Legged Omnidirectional Tetrapod

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27th International Conference on Climbing and Walking Robots and Mobile Machine Support Technologies (CLAWAR)

作者： Karan, Saumya Harikrishnan, R. Dodia, Yukta Phopalkar, Tanish Borate, Suraj Vadali, Madhu Indian Inst Technol Gandhinagar 382055 India

ISBN: (纸本)9783031713002;9783031713019

Soft-legged robots are inherently more compliant to the operating environment. Rigid-legged robots require several degrees of freedom to achieve motion in all directions. This paper introduces a novel, low-cost and simple design of a soft-legged quadruped called SLOT (soft-legged omnidirectional tetrapod), which is capable of motion in all six degrees of freedom. Crawling, walking, sitting, standing, moving in longitudinal and lateral directions, turning left and right, and pitch and roll motion are achieved by only using four motors. SLOT can be operated wirelessly using Bluetooth or autonomously. Pitch, roll and height control is achieved by controlling the contraction of the legs using PID control. Handcrafted gaits are developed for crawl, turn, longitudinal, lateral and omnidirectional walk. A PID-based autonomous longitudinal control based on an ultrasonic sensor has also been demonstrated. Videos are available at https://***/Saumya-Karan/SLOT

关键词： Soft robotics Flexible legged quadruped Gait Central pattern generator PID

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The Automatic Seaman: From Speech2text to Text2Task 6th

The Automatic Seaman: From Speech2text to Text2Task

引用

6th Iberian Robotics Conference (Robot)

作者： Rodrigues, Vitor Borges Lobo, Victor Portuguese Naval Acad Res Ctr CINAV P-2810 Lisbon Portugal

ISBN: (纸本)9783031591662;9783031591679

This paper introduces the development of an autonomous seaman system, leveraging sound-to-text processing, intent detection, slot filling and action control. The Whisper model was employed to process sound commands and transcribe them, while JointBert was used to extract intentions and fill relevant slots. For enhanced model performance, Whisper model was fine-tuned using real voice data in the Portuguese language, while JointBert benefited from data generated by Chat GPT-3. To ensure effective interaction management and action execution, a directed graph structure was used as abstraction. The system's performance was evaluated based on word error rate, intent accuracy, F1 score for slot filling and task accomplished. Experimental results show-cased the effectiveness of our proposed approach, demonstrating accurate comprehension of sound commands and efficient action control. As a result, the autonomous seaman robot holds great promise for practical applications in automating diverse seafaring tasks. The improvement in the man-machine interface is very relevant for manned systems, but even more for unmanned robotic systems.

关键词： Military Robotics Ship Automation systems Multi-Modal Models Language Models

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From Longitudinal Measurements to Image Classification: Application to Longitudinal MRI in Alzheimer's Disease

From Longitudinal Measurements to Image Classification: Appl...

引用

Intelligent systems Conference (IntelliSys)

作者： Mofrad, Samaneh A. Bartsch, Hauke Lundervold, Alexander S. Western Norway Univ Appl Sci Dept Comp Sci Elect Engn & Math Sci Bergen Norway Inmeta Consulting AS Part Crayon Grp Oslo Norway Haukeland Hosp Dept Radiol Mohn Med Imaging & Visualizat Ctr MMIV Bergen Norway Univ Bergen Dept Informat Bergen Norway

ISBN: (纸本)9783031477171;9783031477188

We propose a method of constructing representations of multiple one-dimensional longitudinal measurements as two-dimensional grey-scale images. This can be used to turn classification problems based on longitudinal data into simpler image classification problems, allowing image-based deep learning methods to be applied to longitudinal measurements. Our approach is applicable to situations with balanced or imbalanced longitudinal data sets and where there are missing data at some time points. To evaluate our approach, we apply it to an important and challenging task: the prediction of dementia from brain volume trajectories derived from longitudinal MRI. We construct an ensemble of convolutional neural network models to classify two groups of subjects: those diagnosed with mild cognitive impairment at all examinations (stable MCI) versus those starting out as MCI but later converting to Alzheimer's disease (converted AD). Models were trained on image representations derived from N = 736 subjects sourced from the ADNI database (471/265 sMCI/cAD). We obtained an accuracy of a resulting ensemble model of 76%, measured on an independent test set. Our approach is simple and easy to apply but competitive (in terms of accuracy), with results reported in other machine learning approaches with similar classification on comparable tasks. This indicates that this approach can lead to useful representations of longitudinal data.

关键词： Alzheimer's disease Mild cognitive impairment Longitudinal data MRI Convolutional neural networks

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Requirements-Team Assignment Model that Ensures Balanced Workload with Differences in Skills in Agile Organizations

Requirements-Team Assignment Model that Ensures Balanced Wor...

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International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Yel, Ibrahim Baysa, Mehmet Emin Sarucan, Ahmet Architect Informat Syst TR-34906 Istanbul Turkiye Konya Tech Univ TR-42250 Konya Turkiye

ISBN: (纸本)9783031671944;9783031671951

In today's world of information technologies, agile transformations aim to evaluate small requirements and present them to customers in small deliveries in a short time. The requirement items included in the sprints are scored by the agile team and Story Points are assigned. The expectation from the agile team is that after each requirement is finalized, the other requirements within the sprint will be developed by team members with a proactive approach. At this stage, developing the right requirements by the right team and at the same time distributing the workload within the team balanced can be a serious problem that needs to be solved by people in the product owner role and people in the Scrum master role. By distributing balanced story points among team members and assigning the most appropriate requirements to them, the team's performance and customer satisfaction will increase. In this context, the determined criterion weights were defined with fuzzy AHP. Afterwards, the requirements were evaluated by five experts in the field. The evaluation criteria of the teams corresponding to the requirement criteria were used by the same experts to evaluate the teams. A balanced assignment of requirements story points to each team was made based on the differences between the final scores of the requirements and the final scores of the teams. This assignment process was tried with both (Neutrosophic Z Number Set) NZN and NZN-Area Ellipse methods and the results were compared.

关键词： NZN NZN-Area Agile Assignment Balanced Workload

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RPC-Pillars: Radar Point Correction with Radar-PointPillars 1

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18th International Conference on Intelligent Autonomous systems (IAS)

作者： Lee, Min Young Lee, Christina Dao Wen Shen, Lyuyu Ang, Marcelo H., Jr. Natl Univ Singapore Singapore Singapore

ISBN: (数字)9783031448515

ISBN: (纸本)9783031448508;9783031448515

Radar plays a big role in performing perception tasks for Autonomous Driving in adverse weather-such as during rainy, foggy, hazy, and snowy days. In the current era of rising demand for Autonomous Driving, the need for more accurate and versatile perception tasks are also on the rise. Perception task serves as a crucial stage in Autonomous Driving, as its outcome will be streamed down to other tasks such as trajectory prediction and obstacle avoidance. The topic of object detection has recently undergone a lot of investigation using camera and lidar. However, these sensors carry a limitation of performing poorly in adverse weather. Radar comes in handy in bad weather situations, but due to its sparsity, 3D radar object detection performs poorly. In order to tackle this challenge, we propose a novel radar point correction method to improve the radar point cloud density, followed by RPC-Pillars, a novel radar object detection algorithm. It establishes the new state of the art on nuScenes validation, achieving 2.8% higher mAP in the car class.

关键词： Radar Point cloud correction Object detection

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