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arXiv 2024年

作者： Agarwal, Prankur Feichtinger, Kevin Schmid, Klaus Eichelberger, Holger Rabiser, Rick JKU Dynatrace Co-Innovation Lab LIT CPS Lab Dynatrace Research Linz Austria CRC 1608 KASTEL Dependability of Software-intensive Systems Karlsruhe Institute of Technology Germany Software Systems Engineering Institute of Computer Science University of Hildesheim Germany CDL VaSiCS LIT CPS Lab Johannes Kepler University Linz Austria

software product line techniques encourage the reuse and adaptation of software components for creating customized products or software systems. These different product variants have commonalities and differences, which are managed by variability modeling. Over the past three decades, both academia and industry have developed numerous variability modeling methods, each with its own advantages and disadvantages. Many of these methods have demonstrated their utility within specific domains or applications. However, comprehending the capabilities and differences among these approaches to pinpoint the most suitable one for a particular use case remains challenging. Thus, new modeling techniques and tailored tools for handling variability are frequently created. Transitioning between variability models through transformations from different approaches can help in understanding the benefits and drawbacks of different modeling approaches. However, implementing such transformations presents challenges, such as semantic preservation and avoiding information loss. TRAVART is a tool that helps with transitioning between different approaches by enabling the transformation of variability models into other variability models of different types. This paper discusses the challenges for such transformations between UVL and IVML. It also presents a one-way transformation from the UVL to IVML with as little information loss as possible. © 2024, CC BY.

关键词： Computer software reusability

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Quality of Service (QoS)-Driven Edge Computing and Smart Hospitals: A Vision, Architectural Elements, and Future Directions 1

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NIELIT's International Conference on Communication, Electronics and Digital Technology, NICE-DT 2023

作者： Buyya, Rajkumar Srirama, Satish N. Mahmud, Redowan Goudarzi, Mohammad Ismail, Leila Kostakos, Vassilis Lab School of Computing and Information Systems The University of Melbourne Melbourne Australia School of Computer and Information Sciences The University of Hyderabad Hyderabad India School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia Lab Department of Computer Science and Software Engineering National Water and Energy Center United Arab Emirates University Abu Dhabi United Arab Emirates

ISBN: (数字)9789819916993

ISBN: (纸本)9789819916986

The Internet of Things (IoT) paradigm is drastically changing our world by making everyday objects an integral part of the Internet. This transformation is increasingly being adopted in the healthcare sector, where Smart Hospitals are now relying on IoT technologies to track staff, patients, devices, and equipment, both within a hospital and beyond. This paradigm opens the door to new innovations for creating novel types of interactions among objects, services, and people in smarter ways to enhance the quality of patient services and the efficient utilisation of resources. However, the realisation of real-time IoT applications in health care and, ultimately, the development of Smart Hospitals are constrained by their current Cloud-based computing environment. Edge computing emerged as a new computing model that harnesses Edge-based resources alongside Clouds for real-time IoT applications. It helps to capitalise on the potential economic impact of the IoT paradigm of $11 trillion per year, with a trillion IoT devices deployed by 2025 to sense, manage, and monitor the hospital systems in real time. This vision paper proposes new algorithms and software systems to tackle important challenges in Edge computing-enabled Smart Hospitals, including how to manage and execute diverse real-time IoT applications and how to meet their diverse and strict Quality of Service (QoS) requirements in hospital settings. The vision we outline can help tackle timely challenges that hospitals increasingly face. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quality of service

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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation 14

Towards a Deep Learning Pain-Level Detection Deployment at U...

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14th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Ismail, Leila Waseem, Muhammad Danish Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately. © 2023 Elsevier B.V.. All rights reserved.

关键词： Computer vision

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

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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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A Comprehensive Evaluation on Event Reasoning of Large Language Models 39

A Comprehensive Evaluation on Event Reasoning of Large Langu...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Tao, Zhengwei Jin, Zhi Zhang, Yifan Chen, Xiancai Zhao, Haiyan Li, Jia Liang, Bin Tao, Chongyang Liu, Qun Wong, Kam-Fai School of Computer Science Peking University China MoE Key Lab. of High Confidence Software Technologies(PKU) China Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong MoE Key Lab. of High Confidence Software Technologies(Hong Kong) China Beihang University China Huawei Noah’s Ark Lab China

ISBN: (纸本)157735897X

Event reasoning is a fundamental ability that underlies many applications. It requires event schema knowledge to perform global reasoning and needs to deal with the diversity of the inter-event relations and the reasoning paradigms. The extent to which LLMs excel in event reasoning across various relations and reasoning paradigms has not been thoroughly investigated. Additionally, it is still unclear whether LLMs utilize event knowledge in the same way humans do. To mitigate this disparity, we comprehensively evaluate the abilities of event reasoning of LLMs on different relations, paradigms, and levels of abstraction. We introduce a novel benchmark EV² for EValuation of EVent reasoning. EV²consists of two levels of evaluation on schema and instance and is comprehensive in relations and reasoning paradigms. We conduct extensive experiments on EV². We find that 1) LLMs have abilities to accomplish event reasoning but their performances are far from satisfactory. 2) There are imbalances of event reasoning abilities on different relations and paradigms. 3) LLMs have event schema knowledge, however, they’re not aligned with humans on how to utilize the knowledge. Based on these findings, we guide the LLMs in utilizing the event schema knowledge as memory for improvements in event reasoning. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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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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