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IEEE International Workshop on Measurements and Networking Proceedings (M&N)

作者： Hossein Khalilnasl Salvatore Dello Iacono Paolo Ferrari Alessandra Flammini Brian McCarthy Emiliano Sisinni Dept. of Information Engineering University of Brescia Brescia Italy Sch. of Computer Science and Inf. Technology University College Cork Cork Ireland

ISBN: (数字)9798350370539

ISBN: (纸本)9798350370546

There is clear evidence indicating that the standardized Sensing-Based Semi-Persistent Scheduling scheme in Out-of-coverage possesses specific vulnerabilities. This unsatisfactory performance lies in the absence of coordination in channel scheduling and transmission time. Specifically, the determination to re-assign a resource after the reservation period expires is currently based on a random selection process, without consideration of the actual interference encountered by the utilized resources. In this study, a novel scheme is proposed to markedly improve the random selection method for vehicle positioning.

关键词： Interference Sensors Synchronization Standards

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Using Object Detection to Select Grasp Type & Control Functional Electrical Stimulation for Hand Rehabilitation

Using Object Detection to Select Grasp Type & Control Functi...

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International Conference on Signal Processing and Integrated Networks (SPIN)

作者： Nikunj Arunkumar Bhagat Manikanta Ruppa Dept. of Electrical Engineering Indian Institute of Science Bengaluru India Dept. of Computer Science Indian Inst. Of Inf. Tech. & Mgmt Thiruvananthapuram India

Unilateral muscle weakness and hand paralysis is the most common outcome after a stroke. Functional electrical stimulation (FES) is effective in assisting hand prehension, but conventional stimulators require users to manually select the grasp type (e.g. by pressing a button), which is challenging for patients with severe paralysis. Also, patients need to frequently divert attention from their task to operate the stimulator, which is cumbersome and reduces their engagement in the therapy. In this study, we develop a novel deep learning-based object detection approach to select multiple grasp types and control an electrical stimulator, in order to assist grasping. Object detection was performed using a state-of-the-art YOLOv5 algorithm, which achieved above 93% mean average precision. The algorithm tracked the positions of the hand and objects and selected a grasp type based on the object nearest to the hand. Once the grasp type was selected, a custom-built FES stimulator was activated to execute pre-defined stimulation sequences and allow a person to grasp the nearest object. This contactless, vision-based solution is beneficial for patients opting for homebased rehabilitation since it doesn’t require additional setup time or help from caregivers. The future scope of this work includes testing the object detection-based FES on stroke patients and determining its efficacy in restoring hand movements.

关键词： Signal processing algorithms Object detection Pressing Stroke (medical condition) Signal processing Iron Paralysis

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IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW

作者： Felix Dobslaw Robert Feldt Dept. of Communication Quality Mngmt. and Inf. Systems Mid Sweden University Östersund Sweden Dept. of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden

This study examines to what extent the testing of traditional software components and machine learning (ML) models fundamentally differs or not. While some researchers argue that ML software requires new concepts and perspectives for testing, our analysis highlights that, at a fundamental level, the specification and testing of a software component are not dependent on the development process used or on implementation details. Although the software engineering/computer science (SE/CS) and Data science/ML (DS/ML) communities have developed different expectations, unique perspectives, and varying testing methods, they share clear commonalities that can be leveraged. We argue that both areas can learn from each other, and a non-dual perspective could provide novel insights not only for testing ML but also for testing traditional software. Therefore, we call upon researchers from both communities to collaborate more closely and develop testing methods and tools that can address both traditional and ML software components. While acknowledging their differences has merits, we believe there is great potential in working on unified methods and tools that can address both types of software.

关键词： Software testing Conferences Machine learning Software Testing

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Bridging the Capacity Gap for Online Knowledge Distillation

Bridging the Capacity Gap for Online Knowledge Distillation

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IEEE Conference on Multimedia inf.rmation Processing and Retrieval (MIPR)

作者： Maorong Wang Hao Yu Ling Xiao Toshihiko Yamasaki Dept. Inf. Commun. Eng. The University of Tokyo Tokyo Japan Dept. of Computer Science Boston University Boston USA

Online knowledge distillation (KD) has drawn increasing attention in recent years. However, little attention has been paid to the capacity gap that exists between the models in the online KD paradigm. In this work, we investigate the impact of the capacity gap and experimentally verified that a large capacity gap can have a detrimental effect on performance. Moreover, to address this issue, we propose Auxiliary Branch assisted Mutual Learning (ABML), which leverages auxiliary branches to mitigate performance deterioration caused by the capacity gap. Experimental results show that our ABML can significantly improve the performance of online KD even when a capacity gap is present. Our code is available at https://***/maorongwang/ABML.

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Active Learning Efficiency Benchmark for Coreference Resolution Including Advanced Uncertainty Representations

Active Learning Efficiency Benchmark for Coreference Resolut...

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International Association of Institutes of Navigation World Congress (IAIN)

作者： Marko Sahan Václav Šmídl Taro Watanabe Radek Mařík Dept. of Computer Science FEE CTU in Prague Prague Czechia NLP Laboratory Div. of Inf. Science NAIST Nara Japan

Active learning is a powerful technique that accelerates model learning by iteratively expanding training data based on the model's feedback. This approach has proven particularly relevant in natural language processing and other machine learning domains. While active learning has been extensively studied for conventional classification tasks, its application to more specialized tasks like neural coreference resolution has the potential for improvement. In our research, we present a significant advancement by applying active learning to the neural coreference problem, and setting a benchmark of 39 % reduction in required annotations for training data. Simultaneously, it preserves performance compared to the original model trained on the full data. We compare various uncertainty sampling techniques along with Bayesian modifications of coreference resolution models, conducting a comprehensive analysis of annotation efforts. The results demonstrate that the best-performing techniques seek to maximize label annotation in previously chosen documents, showcasing their effectiveness and preserving performance.

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AI-augmented Automation for Real Driving Prediction: an Industrial Use Case

arXiv

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arXiv 2024年

作者： Eramo, Romina Salman, Hamzeh Eyal Spezialetti, Matteo Stern, Darko Quinton, Pierre Cicchetti, Antonio Dept. of Communication Science University of Teramo Italy Dept. of Software Engineering IT Faculty Mutah University Jordan Dept. of Inf. Eng. Comp. Science and Math. University of L'Aquila Italy Dept. of Research Program Management AVL Austria Dept. of Methodology and R&D AVL Austria Dept. of IDT Mälardalen University Sweden

The risen complexity of automotive systems requires new development strategies and methods to master the upcoming challenges. Traditional methods need thus to be changed by an increased level of automation, and a faster continuous improvement cycle. In this context, current vehicle performance tests represent a very time-consuming and expensive task due to the need to perform the tests in real driving conditions. As a consequence, agile/iterative processes like DevOps are largely hindered by the necessity of triggering frequent tests. This paper reports on a practical experience of developing an AI-augmented solution - based on Machine Learning and Model-based engineering - to support continuous vehicle development and testing. In particular, historical data collected in real driving conditions is leveraged to synthesize a high-fidelity driving simulator and hence enable performance tests in virtual environments. Based on this practical experience, this paper also proposes a conceptual framework to support predictions based on real driving behavior. © 2024, CC BY.

关键词： Machine learning

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Wireless Embedded Index Coded Transmissions with Multiple Antennas

Wireless Embedded Index Coded Transmissions with Multiple An...

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International Conference on inf.rmation and Communication Technology Convergence (ICTC)

作者： Heekang Song Kyungrak Son Wan Choi School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon South Korea Dept. of Inf. Commun. Engineering Hankuk University of Foreign Studies (HUFS) Yongin South Korea Institute of New Media and Communications Seoul National University Seoul South Korea Dept. of Electrical and Computer Engineering Seoul National University Seoul South Korea

Embedded index coding, known as device-to-device index coding, is a promising transmission scheme that effectively reduces the number of transmissions required in device-to-device communications. Leveraging spatial multiplexing gain attained with multiple antennas in wireless environment, this paper explores the benefits of spatially multiplexed embedded index codes through the joint design of embedded index coding and multicast beamformers. Through our simulations, we are able to demonstrate significant improvements obtained by the proposed scheme in terms of spatially multiplexed embedded index coding.

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Low-Cost Antenna Optimization by Means of Gradient Search and Principal Components 4

Low-Cost Antenna Optimization by Means of Gradient Search an...

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4th International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2021

作者： Tomasson, Jon Atli Koziel, Slawomir Pietrenko-Dabrowska, Anna Cheng, Qingsha S. School of Science and Engineering Reykjavik University Reykjavik Iceland Gdansk University of Technology Faculty of Electronics Telecommunications and Inf. Poland Southern University of Science and Technology Dept. Electrical and Electronic Eng. Shenzhen China

ISBN: (纸本)9781733509619

The paper discusses low-cost design closure of antenna structures. Our approach exploits trust-region (TR) gradient search with antenna response gradients updated by a sparse application of finite differentiation, limited to the principal components of the Jacobian matrix. Numerical results demonstrate superiority of the method over benchmark procedures: sixty percent savings are achieved compared to the reference algorithm without compromising the design quality. © 2021 Applied Computational Electromagnetics Society (ACES).

关键词： Jacobian matrices

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The SPADES Framework for Scalable Management of Spatio-textual Data 24

The SPADES Framework for Scalable Management of Spatio-textu...

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24th Pan-Hellenic Conference on inf.rmatics, PCI 2020

作者： Vlachou, Akrivi Doulkeridis, Christos Koutroumanis, Nikolaos Poulopoulos, Dimitrios Nørvåg, Kjetil Dept.of Inf. and Com.Syst.Engineering University of Aegean Karlovasi Greece Dept. of Digital Systems University of Piraeus Piraeus Greece Dept. of Computer Science Norwegian University of Science and Technology Trondheim Norway

ISBN: (纸本)9781450388979

This paper presents the research activities in the context of the SPADES project for scalable indexing and processing of big spatial and spatio-textual data. Management of spatio-textual data raises challenges due to the high dimensional nature of text, in combination with the problem of preserving data locality for spatial data. In this paper, we provide an overview of our contributions in this field, both for centralized and parallel processing. We present an indexing layer that supports spatio-textual data, as well as other data types: spatio-temporal, multidimensional as well as semantic data represented in RDF. This is coupled with a processing layer that encompasses algorithms both for centralized and parallel processing. © 2020 ACM.

关键词： Big data

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Assert use and defectiveness in industrial code

arXiv

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arXiv 2021年

作者： Counsell, Steve Hall, Tracy Shippey, Thomas Bowes, David Tahir, Amjed MacDonell, Stephen Dept. of Computer Science Brunel University London United Kingdom Dept. of Computer Science University of Hertfordshire Hertfordshire United Kingdom School of Eng. & Advanced Tech Massey University New Zealand Dept. of Inf. Science University of Otago New Zealand

The use of asserts in code has received increasing attention in the software engineering community in the past few years, even though it has been a recognized programming construct for many decades. A previous empirical study by Casalnuovo showed that methods containing asserts had fewer defects than those that did not. In this paper, we analyze the test classes of two industrial telecom Java systems to lend support to, or refute that finding. We also analyze the physical position of asserts in methods to determine if there is a relationship between assert placement and method defect-proneness. Finally, we explore the role of test method size and the relationship it has with asserts. In terms of the previous study by Casalnuovo, we found only limited evidence to support the earlier results. We did however find that defective methods with one assert tended to be located at significantly lower levels of the class position-wise than non-defective methods. Finally, method size seemed to correlate strongly with asserts, but surprisingly less so when we excluded methods with just one assert. The work described highlights the need for more studies into this aspect of code, one which has strong links with code comprehension. Copyright © 2021, The Authors. All rights reserved.

关键词： Defects

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