| 1. |
- Isaksson, Martin, et al.
(författare)
-
Adaptive Expert Models for Federated Learning
- 2023
-
Ingår i: <em>Lecture Notes in Computer Science </em>Volume 13448 Pages 1 - 16 2023. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783031289958 ; 13448 LNAI, s. 1-16
-
Konferensbidrag (refereegranskat)abstract
- Federated Learning (FL) is a promising framework for distributed learning when data is private and sensitive. However, the state-of-the-art solutions in this framework are not optimal when data is heterogeneous and non-IID. We propose a practical and robust approach to personalization in FL that adjusts to heterogeneous and non-IID data by balancing exploration and exploitation of several global models. To achieve our aim of personalization, we use a Mixture of Experts (MoE) that learns to group clients that are similar to each other, while using the global models more efficiently. We show that our approach achieves an accuracy up to 29.78% better than the state-of-the-art and up to 4.38% better compared to a local model in a pathological non-IID setting, even though we tune our approach in the IID setting. © 2023, The Author(s)
|
|
| 2. |
- Aramrattana, Maytheewat, 1988-, et al.
(författare)
-
Team Halmstad Approach to Cooperative Driving in the Grand Cooperative Driving Challenge 2016
- 2018
-
Ingår i: IEEE transactions on intelligent transportation systems (Print). - Piscataway, N.J. : Institute of Electrical and Electronics Engineers Inc.. - 1524-9050 .- 1558-0016. ; 19:4, s. 1248-1261
-
Tidskriftsartikel (refereegranskat)abstract
- This paper is an experience report of team Halmstad from the participation in a competition organised by the i-GAME project, the Grand Cooperative Driving Challenge 2016. The competition was held in Helmond, The Netherlands, during the last weekend of May 2016. We give an overview of our car’s control and communication system that was developed for the competition following the requirements and specifications of the i-GAME project. In particular, we describe our implementation of cooperative adaptive cruise control, our solution to the communication and logging requirements, as well as the high level decision making support. For the actual competition we did not manage to completely reach all of the goals set out by the organizers as well as ourselves. However, this did not prevent us from outperforming the competition. Moreover, the competition allowed us to collect data for further evaluation of our solutions to cooperative driving. Thus, we discuss what we believe were the strong points of our system, and discuss post-competition evaluation of the developments that were not fully integrated into our system during competition time. © 2000-2011 IEEE.
|
|
| 3. |
- Lidstrom, D, et al.
(författare)
-
Agent based match racing simulations : Starting practice
- 2022
-
Ingår i: SNAME 24th Chesapeake Sailing Yacht Symposium, CSYS 2022. - : Society of Naval Architects and Marine Engineers.
-
Konferensbidrag (refereegranskat)abstract
- Match racing starts in sailing are strategically complex and of great importance for the outcome of a race. With the return of the America's Cup to upwind starts and the World Match Racing Tour attracting young and development sailors, the tactical skills necessary to master the starts could be trained and learned by means of computer simulations to assess a large range of approaches to the starting box. This project used game theory to model the start of a match race, intending to develop and study strategies using Monte-Carlo tree search to estimate the utility of a player's potential moves throughout a race. Strategies that utilised the utility estimated in different ways were defined and tested against each other through means of simulation and with an expert advice on match racing start strategy from a sailor's perspective. The results show that the strategies that put greater emphasis on what the opponent might do, perform better than those that did not. It is concluded that Monte-Carlo tree search can provide a basis for decision making in match races and that it has potential for further use.
|
|
| 4. |
- Håkansson, Maria, et al.
(författare)
-
Facilitating Mobile Music Sharing and Social Interaction with Push!Music
- 2007
-
Ingår i: Proceedings of the 40th Hawaii International Conference on System Sciences. - Los Alamitos, Calif. : IEEE Computer Society Washington. - 1530-1605. - 0769527558 ; , s. 87-
-
Konferensbidrag (refereegranskat)abstract
- Push!Music is a novel mobile music listening and sharing system, where users automatically receive songs that have autonomously recommended themselves from nearby players depending on similar listening behaviour and music history. Push!Music also enables users to wirelessly send songs between each other as personal recommendations. We conducted a two-week preliminary user study of Push!Music, where a group of five friends used the application in their everyday life. We learned for example that the shared music in Push!Music became a start for social interaction and that received songs in general were highly appreciated and could be looked upon as 'treats'.
|
|
| 5. |
- Martinsson, John, et al.
(författare)
-
Automatic blood glucose prediction with confidence using recurrent neural networks
- 2018
-
Ingår i: CEUR Workshop Proceedings. - : CEUR. ; 2148, s. 64-68
-
Konferensbidrag (refereegranskat)abstract
- Low-cost sensors continuously measuring blood glucose levels in intervals of a few minutes and mobile platforms combined with machine-learning (ML) solutions enable personalized precision health and disease management. ML solutions must be adapted to different sensor technologies, analysis tasks and individuals. This raises the issue of scale for creating such adapted ML solutions. We present an approach for predicting blood glucose levels for diabetics up to one hour into the future. The approach is based on recurrent neural networks trained in an end-to-end fashion, requiring nothing but the glucose level history for the patient. The model outputs the prediction along with an estimate of its certainty, helping users to interpret the predicted levels. The approach needs no feature engineering or data pre-processing, and is computationally inexpensive.
|
|
| 6. |
- Javed, Muhammad, et al.
(författare)
-
Safe and secure platooning of Automated Guided Vehicles in Industry 4.0
- 2021
-
Ingår i: Journal of systems architecture. - Sweden : Elsevier B.V.. - 1383-7621 .- 1873-6165. ; 121
-
Tidskriftsartikel (refereegranskat)abstract
- Automated Guided Vehicles (AGVs) are widely used for materials transportation. Operating them in a platooned manner has the potential to improve safety, security and efficiency, control overall traffic flow and reduce resource usage. However, the published studies on platooning focus mainly on the design of technical solutions in the context of automotive domain. In this paper we focus on a largely unexplored theme of platooning in production sites transformed to the Industry 4.0, with the aim of providing safety and security assurances. We present an overall approach for a fault- and threat tolerant platooning for materials transportation in production environments. Our functional use cases include the platoon control for collision avoidance, data acquisition and processing by considering range, and connectivity with fog and cloud levels. To perform the safety and security analyses, the Hazard and Operability (HAZOP) and Threat and Operability (THROP) techniques are used. Based on the results obtained from them, the safety and security requirements are derived for the identification and prevention/mitigation of potential platooning hazards, threats and vulnerabilities. The assurance cases are constructed to show the acceptable safety and security of materials transportation using AGV platooning. We leveraged a simulation-based digital twin for performing the verification and validation as well as finetuning of the platooning strategy. Simulation data is gathered from digital twin to monitor platoon operations, identify unexpected or incorrect behaviour, evaluate the potential implications, trigger control actions to resolve them, and continuously update assurance cases. The applicability of the AGV platooning is demonstrated in the context of a quarry site. © 2021 The Authors
|
|
| 7. |
- Magnusson, Peter S., et al.
(författare)
-
SimICS/sun4m : A virtual workstation
- 2019
-
Ingår i: USENIX 1998 Annual Technical Conference. - New Orleans, LA, USA : USENIX Association.
-
Konferensbidrag (refereegranskat)abstract
- System level simulators allow computer architects and system software designers to recreate an accurate and complete replica of the program behavior of a target system, regardless of the availability, existence, or instrumentation support of such a system. Applications include evaluation of architectural design alternatives as well as software engineering tasks such as traditional debugging and performance tuning. We present an implementation of a simulator acting as a virtual workstation fully compatible with the sun4m architecture from Sun Microsystems. Built using the system-level SPARC V8 simulator SimICS, SimICS/sun4m models one or more SPARC V8 processors, supports user-developed modules for data cache and instruction cache simulation and execution profiling of all code, and provides a symbolic and performance debugging environment for operating systems. SimICS/sun4m can boot unmodified operating systems, including Linux 2.0.30 and Solaris 2.6, directly from snapshots of disk partitions. To support essentially arbitrary code, we implemented binary-compatible simulators for several devices, including SCSI, console, interrupt, timers, EEPROM, and Ethernet. The Ethernet simulation hooks into the host and allows the virtual workstation to appear on the local network with full services available (NFS, NIS, rsh, etc). Ethernet and console traffic can be recorded for future playback. The performance of SimICS/sun4m is sufficient to run realistic workloads, such as the database benchmark TPC-D, scaling factor 1/100, or an interactive network application such as Mozilla. The slowdown in relation to native hardware is in the range of 25 to 75 (measured using SPECint95). We also demonstrate some applications, including modeling an 8-processor sun4m version (which does not exist), modeling future memory hierarchies, and debugging an operating system.
|
|
| 8. |
- Michel, Mathieu, et al.
(författare)
-
Load-Balanced Data Collection through Opportunistic Routing
- 2015. - 9
-
Ingår i: 2015 International Conference on Distributed Computing in Sensor Systems. - 9781479988563 ; , s. 62-70
-
Konferensbidrag (refereegranskat)abstract
- Wireless Sensor Networks performing low-power data collection often suffer from uneven load distribution among nodes. Nodes close to the network root typically face a higher load, see their battery deplete first, and become prematurely unable to operate (both sensing and relaying other nodes' data). We argue that opportunistic routing, by making forwarding decision on a per-packet basis and at the receiver rather than the sender, has the potential to better balance the load across nodes. We extend ORPL, an opportunistic version of the standard routing protocol RPL, with support for load-balancing. In our protocol, ORPL-LB, nodes continuously adapt their wake-up interval in order to adjust their availability and attain a deployment-specific target duty cycle. We implement our protocol in Contikiand present our experimental validation in Indriya, a 93-nodestestbed. Our results show that ORPL-LB reduces significantly(by approximately 40%) the worst node's duty cycle, with little or no impact on packet delivery ratio and latency.
|
|
| 9. |
- Nazari, N., et al.
(författare)
-
Multi-level Binarized LSTM in EEG Classification for Wearable Devices
- 2020
-
Ingår i: Proceedings - 2020 28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2020. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728165820 ; , s. 175-181
-
Konferensbidrag (refereegranskat)abstract
- Long Short-Term Memory (LSTM) is widely used in various sequential applications. Complex LSTMs could be hardly deployed on wearable and resourced-limited devices due to the huge amount of computations and memory requirements. Binary LSTMs are introduced to cope with this problem, however, they lead to significant accuracy loss in some applications such as EEG classification which is essential to be deployed in wearable devices. In this paper, we propose an efficient multi-level binarized LSTM which has significantly reduced computations whereas ensuring an accuracy pretty close to full precision LSTM. By deploying 5-level binarized weights and inputs, our method reduces area and delay of MAC operation about 31× and 27× in 65nm technology, respectively with less than 0.01% accuracy loss. In contrast to many compute-intensive deep-learning approaches, the proposed algorithm is lightweight, and therefore, brings performance efficiency with accurate LSTM-based EEG classification to realtime wearable devices.
|
|
| 10. |
- Saadatmand, Mehrdad, PhD, 1980-, et al.
(författare)
-
SmartDelta project : Automated quality assurance and optimization across product versions and variants
- 2023
-
Ingår i: Microprocessors and microsystems. - : Elsevier. - 0141-9331 .- 1872-9436. ; 103
-
Tidskriftsartikel (refereegranskat)abstract
- Software systems are often built in increments with additional features or enhancements on top of existing products. This incremental development may result in the deterioration of certain quality aspects. In other words, the software can be considered an evolving entity emanating different quality characteristics as it gets updated over time with new features or deployed in different operational environments. Approaching software development with this mindset and awareness regarding quality evolution over time can be a key factor for the long-term success of a company in today's highly competitive market of industrial software-intensive products. Therefore, it is important to be able to accurately analyze and determine the quality implications of each change and increment to a software system. To address this challenge, the multinational SmartDelta project develops automated solutions for the quality assessment of product deltas in a continuous engineering environment. The project provides smart analytics from development artifacts and system executions, offering insights into quality degradation or improvements across different product versions, and providing recommendations for the next builds. This paper presents the challenges in incremental software development tackled in the scope of the SmartDelta project, and the solutions that are produced and planned in the project, along with the industrial impact of the project for software-intensive industrial systems.
|
|
