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Towards personalized control of things using Arabic voice commands for elderly and with disabilities people

International Journal of Information Technology (Singapore) 2023年 1-22页

作者： Salah, Abdelrahman Adel, Ghada Mohamed, Hussein Baghdady, Youssef Moussa, Sherin M. Department of Software Engineering and Information Technology Faculty of Engineering and Technology Egyptian Chinese University Cairo 11351 Egypt Department of Information Systems Faculty of Computer and Information Sciences Ain Shams University Cairo 11566 Egypt Laboratoire Interdisciplinaire de l’Université Française d’Égypte (UFEID Lab) Université Française d’Égypte Cairo 11837 Egypt

Considering recent smart home solutions that automate home appliances through voice commands, elderly people and/or with special needs prefer using their mother-tongue. However, most of current systems adopt the English language, making it harder for non-native English speakers, while personalization is not heavily considered. In this paper, we propose the Arabic Personalized Control of Things (APCoT) system that achieves inhabitant-tailored home automation through Arabic voice commands based on the user’s preferences to control a home device. A new dataset is created for a wider range of Arabic voice commands to interact with home appliances rather than just turning them on/off. Internet of Things, voice and speech recognition are utilized to attain personalization according to the inhabitants' voiceprints, while infrared is adopted to control non-smart devices supporting infrared signals. As a Proof-of-Concept, three home appliances are adopted: TV, Lights and Bluetooth speaker. Machine learning algorithms are customized for Arabic voice recognition, integrating face and hand gestures recognition to provide an additional facility for inhabitant’s identification and sending control commands respectively. The experimental results indicate that the best average accuracies obtained for Arabic voice recognition were 99.63% for a single speaker and 95.4% for multi-speakers, with average response times 0.141s and 14.29s respectively. For speech recognition, Google online API and a customized Vosk offline pretrained model achieved average accuracies of 95% and 90%, average response times 0.7553s and 0.3933s respectively. The face and hand gestures recognition achieved average accuracies of 90% and 98%, average response times 0.5934s and 15.62049s respectively. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Arabic commands Elderly people Home automation People with disabilities Smart home Speech and voice recognition

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MSFR: Stance Detection Based on Multi-Aspect Semantic Feature Representation via Hierarchical Contrastive Learning

MSFR: Stance Detection Based on Multi-Aspect Semantic Featur...

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

作者： Xuechen Zhao Lei Tian Feng Xie Bin Zhou Haiyang Wang Hongzhou Wu Liqun Gao School of Computer National University of Defense Technology Changsha China Key Lab. of Software Engineering for Complex Systems National University of Defense Technology Changsha China

Zero-shot stance detection aims to determine the stance of previously unseen targets during the inference phase. Achieving effective feature alignment from seen targets to unseen targets is crucial for zero-shot stance detection. In this paper, we propose MSFR, a hierarchical contrastive learning framework, which consists of two core components: inter-aspect contrastive learning for distinguishing aspect-level features and intra-aspect contrastive learning for capturing attribute-level features. Specifically, inter-aspect contrastive learning first maps the global features of an utterance to multiple aspects that influence semantic expression (referred to as aspect-level feature differentiation). This process facilitates the alignment of semantic features across different factors of seen and unseen targets. Intra-aspect contrastive learning enhances the distinguishability of features within the same aspect (referred to as attribute-level feature differentiation) and improves the model’s fine-grained generalization capability. Experimental results demonstrate the superior performance of our model compared to competing baseline models.

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Offline Handwritten Amharic Character Recognition Using Few-Shot Learning 1st

Offline Handwritten Amharic Character Recognition Using Few-...

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1st Pan-African Conference on Artificial Intelligence, PanAfriCon AI 2022

作者： Samuel, Mesay Schmidt-Thieme, Lars Sharma, D.P. Sinamo, Abiot Bruck, Abey Faculty of Computing and Software Engineering Arba Minch University Arba Minch Ethiopia Information Systems and Machine Learning Lab Hildesheim31141 Germany AMUIT MOEFDRE under UNDP Addis Ababa Ethiopia Ministry of Innovation and Technology Federal Democratic Republic of Ethiopia Addis Ababa Ethiopia

ISBN: (纸本)9783031313264

Few-shot learning is a demanding and challenging problem in machine learning aimed at learning from only fewer labeled training examples. It has become an active area of research due to deep learning requiring huge amounts of labeled dataset, which is not easy practically. Learning from fewer examples is also an important attempt towards learning like humans. Few-shot learning has proven a very good promise in different areas of machine learning applications, particularly in image classification. As it is a recent technique, most researchers focus on understanding and solving the issues related to its concept by focusing only on common image datasets like Mini-ImageNet and Omniglot. Few-shot learning also opens an opportunity to address low resource languages like Amharic. Hence in the current study, it is used to address offline handwritten character recognition for Amharic language. Particularly, prototypical networks, the popular and simpler type of few-shot learning, is implemented as a baseline. Using the opportunities explored in the nature of Amharic alphabet having columnwise and rowwise characters’ similarities, a novel way of augmenting the training episodes is proposed. According to the study’s experimental result, it was observed that the proposed method outperformed the baseline method in 1-shot setting. This study has implemented few-shot learning for Amharic characters for the first time. More importantly, the findings of the study open new ways of examining the influence of training episodes in few-shot learning, which is one of the critical issues requiring further exploration. The datasets used for this study are collected from native Amharic language writers using an Android App developed as a part of this study. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Character recognition

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Decentralized Game-based Auction Algorithm for Scheduling Robotic Chargers to Service EVs with Uncertain Demands

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Fang, Qiuyang Zhang, Jianlei Wang, Chen Xie, Guangming Zhang, Chunyan Department of Automation College of Computer and Control Engineering Nankai University Tianjin China National Engineering Research Center of Software Engineering Peking University Beijing China State Key Laboratory for Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China

In this study, we introduce a novel auction-based algorithm modeled as a decentralized coalition formation game, designed for the complex requirements of large-scale multi-robot task allocation under uncertain demand. This context is particularly illustrative in scenarios where robots are tasked to charge electric vehicles. The algorithm begins by partitioning a composite task sequence into distinct subsets based on spatial similarity principles. Subsequently, we employ a coalition formation game paradigm to coordinate the assembly of robots into cooperative coalitions focused on these distinct subsets. To mitigate the impact of unpredictable task demands on allocations, our approach utilizes the conditional value-at-risk to assess the risk associated with task execution, along with computing the potential revenue of the coalition with an emphasis on risk-related outcomes. Additionally, integrating consensus auctions into the coalition formation framework allows our approach to accommodate assignments for individual robot-task pairings, thus preserving the stability of individual robotic decision autonomy within the coalition structure and assignment distribution. Simulative analyses on a prototypical parking facility layout confirm that our algorithm achieves Nash equilibrium within the coalition structure in polynomial time and demonstrates significant scalability. Compared to competing algorithms, our proposal exhibits superior performance in resilience, task execution efficiency, and reduced overall task completion times. The results demonstrate that our approach is an effective strategy for solving the scheduling challenges encountered by multi-robot systems operating in complex environments. IEEE

关键词： Polynomial approximation

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Reinforcement Learning for Blind Stair Climbing with Legged and Wheeled-Legged Robots

arXiv

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arXiv 2024年

作者： Chamorro, Simon Klemm, Victor de la Iglesia Valls, Miguel Pal, Christopher Siegwart, Roland Department of Computer and Software Engineering École Polytechnique de Montréal 2900 Boul. Édouard-Montpetit QC Canada Autonomous Systems Lab ETH Zurich Switzerland Robotic Systems Lab ETH Zurich Switzerland Ascento Robotics Zurich Switzerland

In recent years, legged and wheeled-legged robots have gained prominence for tasks in environments predominantly created for humans across various domains. One significant challenge faced by many of these robots is their limited capability to navigate stairs, which hampers their functionality in multi-story environments. This study proposes a method aimed at addressing this limitation, employing reinforcement learning to develop a versatile controller applicable to a wide range of robots. In contrast to the conventional velocity-based controllers, our approach builds upon a position-based formulation of the RL task, which we show to be vital for stair climbing. Furthermore, the methodology leverages an asymmetric actor-critic structure, enabling the utilization of privileged information from simulated environments during training while eliminating the reliance on exteroceptive sensors during real-world deployment. Another key feature of the proposed approach is the incorporation of a boolean observation within the controller, enabling the activation or deactivation of a stair-climbing mode. We present our results on different quadrupeds and bipedal robots in simulation and showcase how our method allows the balancing robot Ascento to climb 15cm stairs in the real world, a task that was previously impossible for this robot. Copyright © 2024, The Authors. All rights reserved.

关键词： Stairs

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Reinforcement-Learning Based Method for Accelerating Functional Coverage Closure of Traffic Light Controller Dynamic Digital Design 32

Reinforcement-Learning Based Method for Accelerating Functio...

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32nd International Conference on Computer Theory and Applications, ICCTA 2022

作者： Halim, Youstina M. Ismail, Khaled A. Abd El Ghany, Mohamed A. Ibrahim, Sameh A. Halim, Youssef M. Ain Shams University Faculty of Engineering Mixel Egypt. Electronics and Communications Engineering Dept. Cairo Egypt Germany Electronics Department German University in Cairo Egypt Integrated Electronic Systems Lab Tu Darmstadt Germany Ain Shams University Faculty of Engineering Electronics and Communications Engineering Department Cairo Egypt Ain Shams University Faculty of Engineering Computer Engineering and Software Systems Dept. Cairo Egypt

ISBN: (纸本)9798350320190

Functional verification of digital designs is an increasingly complex and time-consuming endeavor. One of the major challenges in functional verification is achieving functional coverage closure in a timely manner. Verification experts have moved from using directed stimulus, towards constrained random stimulus and finally, towards using machine learning to direct stimulus. In this work, reinforcement learning (RL) was used, incorporating three different kinds of RL agents to direct stimulus generation for a traffic light controller system. Two of the agents are based on Q-learning (DQN and SARSA), and one is based on Monte-Carlo method (CEM). The maximum reduction in simulation time needed to achieve a targeted percent of coverage was 97%. The average reduction of simulation time across all testcases and agents used was 70%. The solution is highly reusable, the architecture was integrated with different agents and testcases. © 2022 IEEE.

关键词： Reinforcement learning

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ChatGPT, Let us Chat Sign Language: Experiments, Architectural Elements, Challenges and Research Directions

arXiv

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arXiv 2024年

作者： Shahin, Nada Ismail, Leila Lab Department of CS and Software Engineering College of IT National Water and Energy Center UAE University Al-Ain United Arab Emirates CLOUDS lab School of Computing and Information Systems The University of Melbourne Melbourne Australia

ChatGPT is a language model based on Generative AI. Existing research work on ChatGPT focused on its use in various domains. However, its potential for Sign Language Translation (SLT) is yet to be explored. This paper addresses this void. Therefore, we present GPT's evolution aiming a retrospective analysis of the improvements to its architecture for SLT. We explore ChatGPT's capabilities in translating different sign languages in paving the way to better accessibility for deaf and hard-of-hearing community. Our experimental results indicate that ChatGPT can accurately translate from English to American (ASL), Australian (AUSLAN), and British (BSL) sign languages and from Arabic Sign Language (ArSL) to English with only one prompt iteration. However, the model failed to translate from Arabic to ArSL and ASL, AUSLAN, and BSL to Arabic. Consequently, we present challenges and derive insights for future research directions. © 2024, CC BY.

关键词： Audition

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Closing the Loop between User Stories and GUI Prototypes: An LLM-Based Assistant for Cross-Functional Integration in software Development 25

Closing the Loop between User Stories and GUI Prototypes: An...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing systems

作者： Felix Kretzer Kristian Kolthoff Christian Bartelt Simone Paolo Ponzetto Alexander Maedche Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Institute for Software and Systems Engineering Clausthal University of Technology Clausthal-Zellerfeld Germany Data and Web Science Group University of Mannheim Mannheim Germany human-centered systems lab (h-lab) Karlsruhe Institute of Technology (KIT) Karlsruhe Germany

ISBN: (纸本)9798400713941

Graphical user interfaces (GUIs) are at the heart of almost every software we encounter. GUIs are often created through a collaborative effort involving UX designers, product owners, and software developers, constantly facing changing requirements. Historically, problems in GUI development include a fragmented, poorly integrated tool landscape and high synchronization efforts between stakeholders. Recent approaches suggest using large language models (LLMs) to recognize requirements fulfillment in GUIs and automatically propose new GUI components. Based on ten interviews with practitioners, this paper proposes an LLM-based assistant as a Figma plug-in that bridges the gap between user stories and GUI prototyping. We evaluated the prototype with 40 users and 40 crowd-workers, showing that the effectiveness of GUI creation is improved by using LLMs to detect requirements' completion and generate new GUI components. We derive design rationales to support cross-functional integration in software development, ensuring that our plug-in integrates well into established processes. © 2025 Copyright held by the owner/author(s).

关键词： GUI Prototypes User Stories Requirements Assistance

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Supporting the Episodic Application of Clinical Guidelines over Significant Time Periods

Supporting the Episodic Application of Clinical Guidelines o...

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34th Medical Informatics Europe Conference, MIE 2024

作者： Ben Shahar, Bruria Shahar, Yuval Jaffe, Shai Cohen, Odeya Shalom, Erez Selivanova, Maya Rimon, Ephraim Goldstein, Ayelet Department of Software and Information Systems Engineering Ben-Gurion University Israel Department of Nursing Faculty of Health Sciences Ben-Gurion University Israel Nursing Informatics lab Faculty of Health Sciences Ben-Gurion University Israel Herzfeld Geriatric Rehabilitation Medical Center Clalit Health Services Israel Department of Computer Science Hadassah Academic College Jerusalem Israel

ISBN: (纸本)9781643685335

Medical errors contribute significantly to morbidity and mortality, emphasizing the critical role of Clinical Guidelines (GLs) in patient care. Automating GL application can enhance GL adherence, improve patient outcomes, and reduce costs. However, several barriers exist to GL implementation and real-time automated support. Challenges include creating a formalized, machine-comprehensible GL representation, and an episodic decision-support system for sporadic treatment advice. This system must accommodate the non-continuous nature of care delivery, including partial actions or partially met treatment goals. We describe the design and implementation of an episodic GL-based clinical decision support system and its retrospective technical evaluation using patient records from a geriatric center. Initial evaluation scores of the e-Picard system were promising, with a mean 94% correctness and 90% completeness based on 50 random pressure ulcer patients. Errors were mainly due to knowledge specification, algorithmic issues, and missing data. Post-corrections, scores improved to 100% correctness and a mean 97% completeness, with missing data still affecting completeness. The results validate the system's capability to assess guideline adherence and provide quality recommendations. Despite initial limitations, we have demonstrated the feasibility of providing, through the e-Picard episodic algorithm, realistic medical decision-making support for noncontinuous, intermittent consultations. © 2024 The Authors.

关键词： Random errors

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Cyber-Resilient Edge Computing: A Holistic Approach with Multi-Level MAPE-K Loops

Cyber-Resilient Edge Computing: A Holistic Approach with Mul...

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IEEE International Conference on software Architecture Workshops (ICSAW)

作者： Marco Stadler Johannes Sametinger Michael Riegler Dept. of Business Informatics – Software Engineering LIT Secure and Correct Systems Lab Johannes Kepler University Linz Austria IT Platforms and Operations ENGEL AUSTRIA GmbH Schwertberg Austria

ISBN: (数字)9798350366259

ISBN: (纸本)9798350366266

As edge computing continues to play a pivotal role in modern computing architectures, ensuring robust cybersecurity becomes imperative. This paper introduces our emerging results on a comprehensive approach to bolster the cyber-resilience of edge computing systems by incorporating the MAPE-K (Monitor, Analyze, Plan, Execute, and Knowledge) loop. The proposed methodology involves the application of the MAPE-K loop at different levels of an edge computing architecture, aiming to create a holistic defense mechanism against cyber attacks. We present a prototype of our framework and assess its viability and efficacy by leveraging real-world edge devices deployed in an industrial production setting. Initial evidence from our results suggests that this novel approach leads us to reconsider how we construct more resilient edge computing architectures.

关键词： software architecture Refining Prototypes Computer architecture Production Robustness Monitoring

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