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  • 1.
    Adewumi, Oluwatosin
    et al.
    Luleå University of Technology, Sweden.
    Brännvall, Rickard
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Luleå University of Technology, Sweden.
    Abid, Nosheen
    Luleå University of Technology, Sweden.
    Pahlavan, Maryam
    Luleå University of Technology, Sweden.
    Sabah Sabry, Sana
    Luleå University of Technology, Sweden.
    Liwicki, Foteini
    Luleå University of Technology, Sweden.
    Liwicki, Marcus
    Luleå University of Technology, Sweden.
    Småprat: DialoGPT for Natural Language Generation of Swedish Dialogue by Transfer Learning2022In: Vol. 3 (2022): Proceedings of the Northern Lights Deep Learning Workshop 2022, Septentrio Academic Publishing , 2022, Vol. 3Conference paper (Refereed)
    Abstract [en]

    Building open-domain conversational systems (or chatbots) that produce convincing responses is a recognized challenge. Recent state-of-the-art (SoTA) transformer-based models for the generation of natural language dialogue have demonstrated impressive performance in simulating human-like, single-turn conversations in English.This work investigates, by an empirical study, the potential for transfer learning of such models to Swedish language. DialoGPT, an English language pre-trained model, is adapted by training on three different Swedish language conversational datasets obtained from publicly available sources: Reddit, Familjeliv and the GDC. Perplexity score (an automated intrinsic metric) and surveys by human evaluation were used to assess the performances of the fine-tuned models. We also compare the DialoGPT experiments with an attention-mechanism-based seq2seq baseline model, trained on the GDC dataset. The results indicate that the capacity for transfer learning can be exploited with considerable success. Human evaluators asked to score the simulated dialogues judged over 57% of the chatbot responses to be human-like for the model trained on the largest (Swedish) dataset. The work agrees with the hypothesis that deep monolingual models learn some abstractions which generalize across languages. We contribute the codes, datasets and model checkpoints and host the demos on the HuggingFace platform.

  • 2.
    Afanasov, Mikhail
    et al.
    Politecnico di Milano, Italy; Credit Suisse, Poland.
    Bhatti, Naveed
    Politecnico di Milano, Italy; Air University, Pakistan.
    Campagna, Dennis
    Politecnico di Milano, Italy.
    Caslini, Giacomo
    Politecnico di Milano, Italy.
    Centonze, Fabio
    Politecnico di Milano, Italy.
    Dolui, Koustabh
    Politecnico di Milano, Italy; Ku Leuven, Belgium.
    Maioli, Andrea
    Politecnico di Milano, Italy.
    Barone, Erica
    Microsoft, Italy.
    Alizai, Mohammad
    Lums, Pakistan.
    Siddiqui, Junaid
    Lums, Pakistan.
    Mottola, Luca
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Politecnico di Milano, Italy.
    Battery-less zero-maintenance embedded sensing at the mithræum of circus maximus2020In: SenSys 2020 - Proceedings of the 2020 18th ACM Conference on Embedded Networked Sensor Systems, Association for Computing Machinery, Inc , 2020, p. 368-381Conference paper (Refereed)
    Abstract [en]

    We present the design and evaluation of a 3.5-year embedded sensing deployment at the Mithræum of Circus Maximus, a UNESCO-protected underground archaeological site in Rome (Italy). Unique to our work is the use of energy harvesting through thermal and kinetic energy sources. The extreme scarcity and erratic availability of energy, however, pose great challenges in system software, embedded hardware, and energy management. We tackle them by testing, for the first time in a multi-year deployment, existing solutions in intermittent computing, low-power hardware, and energy harvesting. Through three major design iterations, we find that these solutions operate as isolated silos and lack integration into a complete system, performing suboptimally. In contrast, we demonstrate the efficient performance of a hardware/software co-design featuring accurate energy management and capturing the coupling between energy sources and sensed quantities. Installing a battery-operated system alongside also allows us to perform a comparative study of energy harvesting in a demanding setting. Albeit the latter reduces energy availability and thus lowers the data yield to about 22% of that provided by batteries, our system provides a comparable level of insight into environmental conditions and structural health of the site. Further, unlike existing energy-harvesting deployments that are limited to a few months of operation in the best cases, our system runs with zero maintenance since almost 2 years, including 3 months of site inaccessibility due to a COVID19 lockdown

  • 3.
    Ahmed, Saad
    et al.
    Lahore University of Management Science, Pakistan.
    Bhatti, Naveed
    Air University, Pakistan.
    Alizai, Hamad
    Lahore University of Management Science, Pakistan.
    Siddiqui, Junaid
    Lahore University of Management Science, Pakistan.
    Mottola, Luca
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Politecnico di Milano, Italy.
    Fast and Energy-Efficient State Checkpointing for Intermittent Computing2020In: ACM Transactions on Embedded Computing Systems, ISSN 1539-9087, E-ISSN 1558-3465, Vol. 19, no 6, article id 45Article in journal (Refereed)
    Abstract [en]

    Intermittently powered embedded devices ensure forward progress of programs through state checkpointing in non-volatile memory. Checkpointing is, however, expensive in energy and adds to the execution times. To minimize this overhead, we present DICE, a system that renders differential checkpointing profitable on these devices. DICE is unique because it is a software-only technique and efficient because it only operates in volatile main memory to evaluate the differential. DICE may be integrated with reactive (Hibernus) or proactive (MementOS, HarvOS) checkpointing systems, and arbitrary code can be enabled with DICE using automatic code-instrumentation requiring no additional programmer effort. By reducing the cost of checkpoints, DICE cuts the peak energy demand of these devices, allowing operation with energy buffers that are one-eighth of the size originally required, thus leading to benefits such as smaller device footprints and faster recharging to operational voltage level. The impact on final performance is striking: with DICE, Hibernus requires one order of magnitude fewer checkpoints and one order of magnitude shorter time to complete a workload in real-world settings.

  • 4.
    Ahmed, Saad
    et al.
    Lahore University of Management Sciences, Pakistan.
    Nawaz, Muhammad
    Lahore University of Management Sciences, Pakistan.
    Bakar, Abu
    Lahore University of Management Sciences, Pakistan.
    Bhatti, Naveed Anwar
    Air University, Pakistan.
    Alizai, Muhammad Hamad
    Lahore University of Management Sciences, Pakistan.
    Siddiqui, Junaid Haroon
    Lahore University of Management Sciences, Pakistan.
    Mottola, Luca
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Politecnico di Milano, Italy.
    Demystifying Energy Consumption Dynamics in Transiently Powered Computers2020In: ACM Transactions on Embedded Computing Systems, ISSN 1539-9087, E-ISSN 1558-3465, Vol. 19, no 6, article id 47Article in journal (Refereed)
    Abstract [en]

    Transiently powered computers (TPCs) form the foundation of the battery-less Internet of Things, using energy harvesting and small capacitors to power their operation. This kind of power supply is characterized by extreme variations in supply voltage, as capacitors charge when harvesting energy and discharge when computing. We experimentally find that these variations cause marked fluctuations in clock speed and power consumption. Such a deceptively minor observation is overlooked in existing literature. Systems are thus designed and parameterized in overly conservative ways, missing on a number of optimizations.We rather demonstrate that it is possible to accurately model and concretely capitalize on these fluctuations. We derive an energy model as a function of supply voltage and prove its use in two settings. First, we develop EPIC, a compile-time energy analysis tool. We use it to substitute for the constant power assumption in existing analysis techniques, giving programmers accurate information on worst-case energy consumption of programs. When using EPIC with existing TPC system support, run-time energy efficiency drastically improves, eventually leading up to a 350% speedup in the time to complete a fixed workload. Further, when using EPIC with existing debugging tools, it avoids unnecessary program changes that hurt energy efficiency. Next, we extend the MSPsim emulator and explore its use in parameterizing a different TPC system support. The improvements in energy efficiency yield up to more than 1000% time speedup to complete a fixed workload.

  • 5.
    Alonso, P.
    et al.
    Luleå University of Technology, Sweden.
    Shridhar, K.
    ETH Zürich, Switzerland.
    Kleyko, Denis
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Osipov, E.
    Luleå University of Technology, Sweden.
    Liwicki, M.
    Luleå University of Technology, Sweden.
    HyperEmbed: Tradeoffs between Resources and Performance in NLP Tasks with Hyperdimensional Computing Enabled Embedding of n-gram Statistics2021In: Proceedings of the International Joint Conference on Neural Networks, Institute of Electrical and Electronics Engineers Inc. , 2021, Vol. 2021-JulyConference paper (Refereed)
    Abstract [en]

    Recent advances in Deep Learning have led to a significant performance increase on several NLP tasks, however, the models become more and more computationally demanding. Therefore, this paper tackles the domain of computationally efficient algorithms for NLP tasks. In particular, it investigates distributed representations of n -gram statistics of texts. The representations are formed using hyperdimensional computing enabled embedding. These representations then serve as features, which are used as input to standard classifiers. We investigate the applicability of the embedding on one large and three small standard datasets for classification tasks using nine classifiers. The embedding achieved on par F_1 scores while decreasing the time and memory requirements by several times compared to the conventional n -gram statistics, e.g., for one of the classifiers on a small dataset, the memory reduction was 6.18 times; while train and test speed-ups were 4.62 and 3.84 times, respectively. For many classifiers on the large dataset, memory reduction was ca. 100 times and train and test speed-ups were over 100 times. Importantly, the usage of distributed representations formed via hyperdimensional computing allows dissecting strict dependency between the dimensionality of the representation and n-gram size, thus, opening a room for tradeoffs. 

  • 6.
    Anne Cochrane, Karen
    et al.
    Carleton University, Canada.
    Mah, Kristina
    School of Architecture, Australia.
    Ståhl, Anna
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Núñez-Pacheco, Claudia
    KTH Royal Institute of Technology, Sweden.
    Balaam, Madeline
    KTH Royal Institute of Technology, Sweden.
    Ahmadpour, Naseem
    School of Architecture, Australia.
    Loke, Lian
    School of Architecture, Australia.
    Body Maps: A Generative Tool for Soma-based Design2022In: ACM International Conference Proceeding Series, Association for Computing Machinery , 2022, article id 3502262Conference paper (Refereed)
    Abstract [en]

    Body maps are visual documents, where somatic experiences can be drawn onto a graphical representation of an outline of the human body. They hold the ability to capture complex and non-explicit emotions and somatic felt sensations, elaborating narratives that cannot be simply spoken. We present an illustrative example of "how-to"complete a body map, together with four case studies that provide examples of using body maps in design research. We identify five uses of body maps as generative tools for soma-based design, ranging from sampling bodily experience, heightening bodily self-awareness, understanding changing bodily experience over time, identifying patterns of bodily experience, and transferring somatic experiential qualities into physical designs. The different requirements for scaffolding the use of body maps in user-centred design versus first-person autobiographical design research are discussed. We provide this Pictorial as a resource for designers and researchers who wish to integrate body maps into their practice. © 2022 Owner/Author.

  • 7.
    Armgarth, Astrid
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Smart Hardware. Linköping University, Sweden.
    Pantzare, Sandra
    RISE Research Institutes of Sweden, Digital Systems, Smart Hardware.
    Arven, Patrik
    J2 Holding AB, Sweden.
    Lassnig, Roman
    RISE Research Institutes of Sweden.
    Jinno, Hiroaki
    RIKEN Center for Emergent Matter Science, Japan; University of Tokyo, Japan.
    Gabrielsson, Erik
    Linköping University, Sweden.
    Kifle, Yonatan
    Linköping University, Sweden.
    Cherian, Dennis
    Linköping University, Sweden.
    Arbring Sjöström, Theresia
    Linköping University, Sweden.
    Berthou, Gautier
    RISE Research Institutes of Sweden.
    Dowling, Jim
    RISE Research Institutes of Sweden, Digital Systems, Data Science. KTH Royal Institute of Technology, Sweden.
    Someya, Takao
    RIKEN Center for Emergent Matter Science, Japan; University of Tokyo, Japan.
    Wikner, Jacob
    Linköping University, Sweden.
    Gustafsson, Göran
    RISE Research Institutes of Sweden.
    Simon, Daniel
    Linköping University, Sweden.
    Berggren, Magnus
    Linköping University, Sweden.
    A digital nervous system aiming toward personalized IoT healthcare2021In: Scientific Reports, E-ISSN 2045-2322, Vol. 11, no 1, article id 7757Article in journal (Refereed)
    Abstract [en]

    Body area networks (BANs), cloud computing, and machine learning are platforms that can potentially enable advanced healthcare outside the hospital. By applying distributed sensors and drug delivery devices on/in our body and connecting to such communication and decision-making technology, a system for remote diagnostics and therapy is achieved with additional autoregulation capabilities. Challenges with such autarchic on-body healthcare schemes relate to integrity and safety, and interfacing and transduction of electronic signals into biochemical signals, and vice versa. Here, we report a BAN, comprising flexible on-body organic bioelectronic sensors and actuators utilizing two parallel pathways for communication and decision-making. Data, recorded from strain sensors detecting body motion, are both securely transferred to the cloud for machine learning and improved decision-making, and sent through the body using a secure body-coupled communication protocol to auto-actuate delivery of neurotransmitters, all within seconds. We conclude that both highly stable and accurate sensing—from multiple sensors—are needed to enable robust decision making and limit the frequency of retraining. The holistic platform resembles the self-regulatory properties of the nervous system, i.e., the ability to sense, communicate, decide, and react accordingly, thus operating as a digital nervous system. © 2021, The Author(s).

  • 8.
    Arnelid, Henrik
    et al.
    Zenuity AB, Sweden.
    Listo Zac, Edvin
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Mohammadiha, Nasser
    Zenuity AB, Sweden.
    Recurrent Conditional Generative Adversarial Networks forAutonomous Driving Sensor Modelling2019Conference paper (Refereed)
    Abstract [en]

     Simulation of the real world is a widely researchedtopic in various fields. The automotive industry in particular isvery dependent on real world simulations, since these simulations are needed in order to prove the safety of advance driverassistance systems (ADAS) and autonomous driving (AD). Inthis paper we propose a deep learning based model for simulating the outputs from production sensors used in autonomousvehicles. We introduce an improved Recurrent ConditionalGenerative Adversarial Network (RC-GAN) consisting of Recurrent Neural Networks (RNNs) that use Long Short-TermMemory (LSTM) in both the generator and the discriminatornetworks in order to generate production sensor errors thatexhibit long-term temporal correlations. The network is trainedin a sequence-to-sequence fashion where we condition theoutput from the model on sequences describing the surroundingenvironment. This enables the model to capture spatial andtemporal dependencies, and the model is used to generatesynthetic time series describing the errors in a productionsensor which can be used for more realistic simulations. Themodel is trained on a data set collected from real roads withvarious traffic settings, and yields significantly better results ascompared to previous works.

  • 9.
    Arnell, Magnus
    et al.
    RISE Research Institutes of Sweden, Built Environment, Infrastructure and concrete technology.
    Ahlström, Marcus
    RISE Research Institutes of Sweden, Built Environment, Infrastructure and concrete technology.
    Wärff, Christoffer
    RISE Research Institutes of Sweden, Built Environment, Infrastructure and concrete technology.
    Miltell, Maya
    RISE Research Institutes of Sweden, Digital Systems, Prototyping Society.
    Vahidi, Arash
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Digitalisering av den svenska VA-branschen2021Report (Other academic)
    Abstract [en]

    The report provides a knowledge base on the digital transformation in the water industry, its visionand potential. Key success factors are pointed out and challenges with workforce competence,data management and cybersecurity is outlined. A catalogue with ten examples of successful digitalapplications is provided for inspiration.

    Download full text (pdf)
    Rapport
  • 10.
    Asad, H. A.
    et al.
    Uppsala University, Sweden.
    Wouters, E. H.
    KTH Royal Institute of Technology, Sweden.
    Bhatti, N. A.
    Air University, Pakistan.
    Mottola, Luca
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Voigt, Thiemo
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Uppsala University, Sweden.
    On Securing Persistent State in Intermittent Computing2020In: ENSsys 2020 - Proceedings of the 8th International Workshop on Energy Harvesting and Energy-Neutral Sensing Systems16 November 2020, Pages 8-148th International Workshop on Energy Harvesting and Energy-Neutral Sensing Systems, ENSsys 2020, co-located with ACM SenSys 2020; Virtual, Online; Japan; 16 November 2020 throug, Association for Computing Machinery, Inc , 2020, p. 8-14Conference paper (Refereed)
    Abstract [en]

    We present the experimental evaluation of different security mechanisms applied to persistent state in intermittent computing. Whenever executions become intermittent because of energy scarcity, systems employ persistent state on non-volatile memories (NVMs) to ensure forward progress of applications. Persistent state spans operating system and network stack, as well as applications. While a device is off recharging energy buffers, persistent state on NVMs may be subject to security threats such as stealing sensitive information or tampering with configuration data, which may ultimately corrupt the device state and render the system unusable. Based on modern platforms of the Cortex M*series, we experimentally investigate the impact on typical intermittent computing workloads of different means to protect persistent state, including software and hardware implementations of staple encryption algorithms and the use of ARM TrustZone protection mechanisms. Our results indicate that i) software implementations bear a significant overhead in energy and time, sometimes harming forward progress, but also retaining the advantage of modularity and easier updates; ii) hardware implementations offer much lower overhead compared to their software counterparts, but require a deeper understanding of their internals to gauge their applicability in given application scenarios; and iii) TrustZone shows almost negligible overhead, yet it requires a different memory management and is only effective as long as attackers cannot directly access the NVMs

  • 11.
    Aslam, Mudassar
    et al.
    RISE Research Institutes of Sweden, Digital Systems. COMSATS University Islamabad, Pakistan.
    Bouget, Simon
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Raza, Shahid
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Security and trust preserving inter- and intra-cloud VM migrations2020In: International Journal of Network Management, ISSN 1055-7148, E-ISSN 1099-1190, article id e2103Article in journal (Refereed)
    Abstract [en]

    This paper focus on providing a secure and trustworthy solution for virtual machine (VM) migration within an existing cloud provider domain, and/or to the other federating cloud providers. The infrastructure-as-a-service (IaaS) cloud service model is mainly addressed to extend and complement the previous Trusted Computing techniques for secure VM launch and VM migration case. The VM migration solution proposed in this paper uses a Trust_Token based to guarantee that the user VMs can only be migrated and hosted on a trustworthy and/or compliant cloud platforms. The possibility to also check the compliance of the cloud platforms with the pre-defined baseline configurations makes our solution compatible with an existing widely accepted standards-based, security-focused cloud frameworks like FedRAMP. Our proposed solution can be used for both inter- and intra-cloud VM migrations. Different from previous schemes, our solution is not dependent on an active (on-line) trusted third party; that is, the trusted third party only performs the platform certification and is not involved in the actual VM migration process. We use the Tamarin solver to realize a formal security analysis of the proposed migration protocol and show that our protocol is safe under the Dolev-Yao intruder model. Finally, we show how our proposed mechanisms fulfill major security and trust requirements for secure VM migration in cloud environments. 

  • 12.
    Aslam, Mudassar
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science. COMSATS University Islamabad, Pakistan.
    Mohsin, Bushra
    COMSATS University Islamabad, Pakistan.
    Nasir, Abdul
    COMSATS University Islamabad, Pakistan.
    Raza, Shahid
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    FoNAC - An automated Fog Node Audit and Certification scheme2020In: Computers & security (Print), ISSN 0167-4048, E-ISSN 1872-6208, Vol. 93, article id 101759Article in journal (Refereed)
    Abstract [en]

    Meeting the security and privacy needs for IoT data becomes equally important in the newly introduced intermediary Fog Computing layer, as it was in its former technological layer - Cloud; but the accomplishment of such security is critical and challenging. While security assurance of the fog layer devices is imperative due to their exposure to the public Internet, it becomes even more complex, than the cloud layer, as it involves a large number of heterogeneous devices deployed hierarchically. Manual audit and certification schemes are unsuitable for large number of fog nodes thereby inhibiting the involved stakeholders to use manual security assurance schemes altogether. However, scalable and feasible security assurance can be provided by introducing automated and continuous monitoring and auditing of fog nodes to ensure a trusted, updated and vulnerability free fog layer. This paper presents such an solution in the form of an automated Fog Node Audit and Certification scheme (FoNAC) which guarantees a secure fog layer through the proposed fog layer assurance mechanism. FoNAC leverages Trusted Platform Module (TPM 2.0) capabilities to evaluate/audit the platform integrity of the operating fog nodes and grants certificate to the individual node after a successful security audit. FoNAC security is also validated through its formal security analysis performed using AVISPA under Dolev-Yao intruder model. The security analysis of FoNAC shows its resistance against cyber-attacks like impersonation, replay attack, forgery, Denial of Service(DoS) and MITM attack.

  • 13.
    Balaam, Madeline
    et al.
    KTH Royal Institute of Technology, Sweden.
    Woytuk, Nadia Campo
    KTH Royal Institute of Technology, Sweden.
    Felice, Marianela Ciolfi
    KTH Royal Institute of Technology, Sweden.
    Afsar, Ozgun Kilic
    KTH Royal Institute of Technology, Sweden.
    Ståhl, Anna
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Søndergaard, Marie Louise Juul
    KTH Royal Institute of Technology, Sweden.
    Intimate touch2020In: interactions, ISSN 1072-5520, E-ISSN 1558-3449, Vol. 27, no 6, p. 14-17Article in journal (Refereed)
  • 14.
    Battaglioli, S.
    et al.
    Trinity College Dublin, Ireland.
    Lebon, M.
    Nexalus Labs, Ireland.
    Jenkins, R.
    Nexalus Labs, Ireland.
    Summers, Jon
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Luleå University of Technology, Sweden.
    Sarkinen, Jeffrey
    RISE Research Institutes of Sweden. Luleå University of Technology, Sweden.
    Robinson, A. J.
    Trinity College Dublin, Ireland.
    Enhancement of an Open Compute Project (OCP) server thermal management and waste heat recovery potential via hybrid liquid-cooling2022In: THERMINIC 2022 - 28th International Workshop on Thermal Investigations of ICs and Systems, Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2022Conference paper (Refereed)
    Abstract [en]

    A multiphysics Simulation-Driven Design approach has been undertaken to augment the OCP Leopard Server thermal management and heat recovery hardware with the Nexalus hybrid liquid-cooled sealed server technology. Independent testing at the RISE Research Institute of Sweden has proven up to 98% heat recovery is achievable at water temperatures up to and exceeding 65°C. The improved design could maintain the elevated water temperature over a range of CPU workloads, ranging from 8% to 75%. Importantly, the design solution achieves this within an architecture that is IOU in height, half that of the original stock 20U server, potentially doubling the compute density of a rack. 

  • 15.
    Behravesh, Rasaoul
    et al.
    Fondazione Bruno Kessler, Italy.
    Rao, Akhila
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Perez-Ramirez, Daniel F.
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Harutyunyan, Davit
    Robert Bosch GmbH, Germany.
    Riggio, Roberto
    Politecnica delle Marche, Italy.
    Boman, Magnus
    Robert Bosch GmbH, Germany.
    Machine Learning at the Mobile Edge: The Case of Dynamic Adaptive Streaming over HTTP (DASH)2022In: IEEE Transactions on Network and Service Management, ISSN 1932-4537, E-ISSN 1932-4537, Vol. 19, no 4, p. 4779-4793Article in journal (Refereed)
    Abstract [en]

    Dynamic Adaptive Streaming over HTTP (DASH) is a standard for delivering video in segments and adapting each segment’s bitrate (quality), to adjust to changing and limited network bandwidth. We study segment prefetching, informed by machine learning predictions of bitrates of client segment requests, implemented at the network edge. We formulate this client segment request prediction problem as a supervised learning problem of predicting the bitrate of a client’s next segment request, in order to prefetch it at the mobile edge, with the objective of jointly improving the video streaming experience for the users and network bandwidth utilization for the service provider. The results of extensive evaluations showed a segment request prediction accuracy of close to 90% and reduced video segment access delay with a cache hit ratio of 58%, and reduced transport network load by lowering the backhaul link utilization by 60.91%.

  • 16.
    Behravesh, Rasoul
    et al.
    University of Bologna, Italy.
    Harutyunyan, Davit
    Robert Bosch GmbH, Germany.
    Coronado, Estefania
    I2CAT Foundation, Spain.
    Riggio, Roberto
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Time-Sensitive Mobile User Association and SFC Placement in MEC-Enabled 5G Networks2021In: IEEE Transactions on Network and Service Management, ISSN 1932-4537, E-ISSN 1932-4537, Vol. 18, no 3, p. 3006-3020Article in journal (Refereed)
    Abstract [en]

    The ongoing roll-out of 5G networks paves the way for many fascinating applications such as virtual reality (VR), augmented reality (AR), and autonomous driving. Moreover, 5G enables billions of devices to transfer an unprecedented amount of data at the same time. This transformation calls for novel technologies like multi-access edge computing (MEC) to satisfy the stringent latency and bitrate requirements of the mentioned applications. The main challenge pertaining to MEC is that the edge MEC nodes are usually characterized by scarce computational resources compared to the core or cloud, arising the challenge of efficiently utilizing the edge resources while ensuring that the service requirements are satisfied. When considered with the users’ mobility, this poses another challenge, which lies in minimization of the service interruption for the users whose service requests are represented as service function chains (SFCs) composed of virtualized network functions (VNFs) instantiated on the MEC nodes or on the cloud. In this paper, we study the problem of joint user association, SFC placement, and resource allocation, employing mixed-integer linear programming (MILP) techniques. The objective function of this MILP-based problem formulation are to minimize (i) the service provisioning cost, (ii) the transport network utilization, and (iii) the service interruption. Moreover, a heuristic algorithm is proposed to tackle the scalability issue of the MILP-based algorithms. Finally, comprehensive experiments are performed to draw a comparison between these approaches.

  • 17.
    Behravesh, Rasoul
    et al.
    Fondazione Bruno Kessler, Italy; University of Bologna, Italy.
    Perez-Ramirez, Daniel F.
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Rao, Akhila
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Harutyunyan, Davit
    Fondazione Bruno Kessler, Italy.
    Riggio, Roberto
    Fondazione Bruno Kessler, Italy.
    Steinert, Rebecca
    RISE Research Institutes of Sweden.
    ML-Driven DASH Content Pre-Fetching in MEC-Enabled Mobile Networks2020In: 16th International Conference on Network and Service Management, CNSM 2020, 2nd International Workshop on Analytics for Service and Application Management, AnServApp 2020 and 1st International Workshop on the Future Evolution of Internet Protocols, IPFuture 2020, Institute of Electrical and Electronics Engineers Inc. , 2020Conference paper (Refereed)
    Abstract [en]

    Streaming high-quality video over dynamic radio networks is challenging. Dynamic adaptive streaming over HTTP (DASH) is a standard for delivering video in segments, and adapting its quality to adjust to a changing and limited network bandwidth. We present a machine learning-based predictive pre-fetching and caching approach for DASH video streaming, implemented at the multi-access edge computing server. We use ensemble methods for machine learning (ML) based segment request prediction and an integer linear programming (ILP) technique for pre-fetching decisions. Our approach reduces video segment access delay with a cache-hit ratio of 60% and alleviates transport network load by reducing the backhaul link utilization by 69%. We validate the ML model and the pre-fetching algorithm, and present the trade-offs involved in pre-fetching and caching for resource-constrained scenarios.

  • 18.
    Berezovskaya, Yulia
    et al.
    Luleå University of Technology, Sweden.
    Yang, Chen Wei
    Luleå University of Technology, Sweden.
    Mousavi, Arash
    SCANIA CV AB, Sweden.
    Vyatkin, Valeriy
    Luleå University of Technology, Sweden.
    Minde, Tor Björn
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Modular Model of a Data Centre as a Tool for Improving Its Energy Efficiency2020In: IEEE Access, E-ISSN 2169-3536, Vol. 8, p. 46559-46573Article in journal (Refereed)
    Abstract [en]

    For most modern data centres, it is of high value to select practical methods for improving energy efficiency and reducing energy waste. IT-equipment and cooling systems are the two most significant energy consumers in data centres, thus the energy efficiency of any data centre mainly relies on the energy efficiency of its computational and cooling systems. Existing techniques of optimising the energy usage of both these systems have to be compared. However, such experiments cannot be conducted in real plants as they may harm the electronic equipment. This paper proposes a modelling toolbox which enables building models of data centres of any scale and configuration with relative ease. The toolbox is implemented as a set of building blocks which model individual components of a typical data centre, such as processors, local fans, servers, units of cooling systems, it provides methods of adjusting the internal parameters of the building blocks, as well as contains constructors utilising the building blocks for building models of data centre systems of different levels from server to the server room. The data centre model is meant to accurate estimating the energy consumption as well as the evolution of the temperature of all computational nodes and the air temperature inside the data centre. The constructed model capable of substitute for the real data centre at examining the performance of different energy-saving strategies in dynamic mode: the model provides information about data centre operating states at each time point (as model outputs) and takes values of adjustable parameters as the control signals from system implementing energy-saving algorithm (as model inputs). For Module 1 of the SICS ICE data centre located in Luleå, Sweden, the model was constructed from the building blocks. After adjusting the internal parameters of the building blocks, the model demonstrated the behaviour quite close to real data from the SICS ICE data centre. Therefore the model is applicable to use as a substitute for the real data centre. Some examples of using the model for testing energy-saving strategies are presented at the end of the paper.

  • 19.
    Bosk, Daniel
    et al.
    KTH Royal Institute of Technology, Sweden.
    Bouget, Simon
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Buchegger, Sonja
    KTH Royal Institute of Technology, Sweden.
    Distance-bounding, privacy-preserving attribute-based credentials2020In: International Conference on Cryptology and Network SecurityCANS 2020: Cryptology and Network Security, Springer Science and Business Media Deutschland GmbH , 2020, p. 147-166Conference paper (Refereed)
    Abstract [en]

    Distance-bounding anonymous credentials could be used for any location proofs that do not need to identify the prover and thus could make even notoriously invasive mechanisms such as location-based services privacy-preserving. There is, however, no secure distance-bounding protocol for general attribute-based anonymous credentials. Brands and Chaum’s (EUROCRYPT’93) protocol combining distance-bounding and Schnorr identification comes close, but does not fulfill the requirements of modern distance-bounding protocols. For that, we need a secure distance-bounding zero-knowledge proof-of-knowledge resisting mafia fraud, distance fraud, distance hijacking and terrorist fraud. Our approach is another attempt toward combining distance bounding and Schnorr to construct a distance-bounding zero-knowledge proof-of-knowledge. We construct such a protocol and prove it secure in the (extended) DFKO model for distance bounding. We also performed a symbolic verification of security properties needed for resisting these attacks, implemented in Tamarin. Encouraged by results from Singh et al. (NDSS’19), we take advantage of lessened constraints on how much can be sent in the fast phase of the distance-bounding protocol and achieve a more efficient protocol. We also provide a version that does not rely on being able to send more than one bit at a time which yields the same properties except for (full) terrorist fraud resistance.

  • 20.
    Bozic Yams, Nina
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Richardson, Valerie
    Gradient Descent, Sweden.
    Shubina, Galina
    Gradient Descent, Sweden.
    Albrecht, Sandor
    RISE Research Institutes of Sweden, Digital Systems, Data Science. WALP, Sweden.
    Gillblad, Daniel
    RISE Research Institutes of Sweden. AI Sweden, Sweden.
    Integrated ai and innovationmanagement: The beginning of a beautiful friendship2021In: Technology Innovation Management Review, E-ISSN 1927-0321, Vol. 10, no 11, p. 5-18Article in journal (Refereed)
    Abstract [en]

    There is a growing consensus around the transformative and innovative power of Artificial Intelligence (AI) technology. AI will transform which products are launched and how new business models will be developed to support them. Despite this, little research exists today that systematically explores how AI will change and support various aspects of innovation management. To address this question, this article proposes a holistic, multi-dimensional AI maturity model that describes the essential conditions and capabilities necessary to integrate AI into current systems, and guides organisations on their journey to AI maturity. It explores how various elements of the innovation management system can be enabled by AI at different maturity stages. Two key experimentation stages are identified, 1) an initial stage that focuses on optimisation and incremental innovation, and 2) a higher maturity stage where AI becomes an enabler of radical innovation. We conclude that AI technologies can be applied to democratise and distribute innovation across organisations.

  • 21.
    Bozic Yams, Nina
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Richardson, Valerie
    Gradient Descent, Sweden.
    Shubina, Galina Esther
    Gradient Descent, Sweden.
    Albrecht, Sandor
    RISE Research Institutes of Sweden, Digital Systems, Data Science. WALP, Sweden.
    Gillblad, Daniel
    RISE Research Institutes of Sweden. AI Sweden, Sweden.
    Integrated AI and Innovation Management: The Beginning of a Beautiful Friendship2020In: Technology Innovation Management Review, Vol. 10, no 11Article in journal (Refereed)
    Abstract [en]

    There is a growing consensus around the transformative and innovative power of Artificial Intelligence (AI) technology. AI will transform which products are launched and how new business models will be developed to support them. Despite this, little research exists today that systematically explores how AI will change and support various aspects of innovation management. To address this question, this article proposes a holistic, multi-dimensional AI maturity model that describes the essential conditions and capabilities necessary to integrate AI into current systems, and guides organisations on their journey to AI maturity. It explores how various elements of the innovation management system can be enabled by AI at different maturity stages. Two key experimentation stages are identified, 1) an initial stage that focuses on optimisation and incremental innovation, and 2) a higher maturity stage where AI becomes an enabler of radical innovation. We conclude that AI technologies can be applied to democratise and distribute innovation across organisations.

  • 22.
    Broberg, Johan
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Bånkestad, Maria
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Uppsala University, Sweden.
    Ylipää, Erik
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Pre-training Transformers for Molecular Property Prediction Using Reaction Prediction2022Conference paper (Refereed)
    Abstract [en]

    Molecular property prediction is essential in chemistry, especially for drug discovery applications. However, available molecular property data is often limited, encouraging the transfer of information from related data. Transfer learning has had a tremendous impact in fields like Computer Vision and Natural Language Processng signaling for its potential in molecular property prediction. We present a pre-training procedure for molecular representation learning using reaction data and use it to pre-train a SMILES Transformer. We fine-tune and evaluate the pretrained model on 12 molecular property prediction tasks from MoleculeNet within physical chemistry, biophysics, and physiology and show a statistically significant positive effect on 5 of the 12 tasks compared to a non-pre-trained baseline model.

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  • 23.
    Brunklaus, Birgit
    et al.
    RISE Research Institutes of Sweden, Built Environment, System Transition and Service Innovation.
    Diener, Derek
    RISE Research Institutes of Sweden, Built Environment, System Transition and Service Innovation.
    Enebog, Emma
    RISE Research Institutes of Sweden, Materials and Production, Product Realisation Methodology.
    Hautajärvi Stenmark, Heidi
    RISE Research Institutes of Sweden, Materials and Production, Product Realisation Methodology.
    Lundahl, Jenny
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.
    Matteoni, Marina
    RISE Research Institutes of Sweden, Built Environment, System Transition and Service Innovation.
    Nyström, Thomas
    RISE Research Institutes of Sweden, Built Environment, System Transition and Service Innovation.
    Nilsson-Lindén, Hanna
    RISE Research Institutes of Sweden, Materials and Production, Product Realisation Methodology.
    Renström, Sara
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Den cirkulära bilen (förstudie)2023Report (Other academic)
    Abstract [sv]

    Syftet med förstudien Den cirkulära bilen var att börja bygga konkreta visioner som möjliggör att Sverige har en cirkulärt anpassad bilflotta med fossilfria och klimatneutrala transporter år 2045 och att bygga en solid bas för ett steg 2-projekt, som i sin tur kommer att ge stöd och kapacitet för aktörer att accelerera den cirkulära bilvärdekedjan. Projektet har samlat 13 parter från hela värdekedjan och gemensamt lagt grunden till vidare arbete i ett fortsättningsprojekt – en ansökan som genererat intresse från ett stort antal parter både befintliga och nytillkommande. Inom studien har startmöten och workshops genomförts där parter samlats digitalt och frågeställningar sonderats. Intervjuer har genomförts med parter där möjligheter och utmaningar med omställningen diskuterats. Studiebesök har genomförts där kunskapsdelning skett och samverkan möjliggjorts. Fysisk workshop har genomförts med samtliga parter. Här tittade man gemensamt på trender och möjliga framtidsscenarios genom hela systemet. Detta gav en bra grund för det vidare arbetet med steg 2. Förstudien har genererat stort intresse från aktörer i hela värdekedjan, skapat nya kontakter och möjligheter till samverkan och blivit uppstarten på en gemensam kunskapsresa för verklig förändring. Studien har initierat arbete brett i värdekedjan kopplat till gemensamma frågeställningar samt framtidsspaningar, vilket möjliggör gemensamt arbete för bred omställning och tydliggjort behovet av åtgärder som förflyttar hela systemet. Detta ses som en god grund för ett steg 2 projekt med förutsättningar för att förverkliga den cirkulära bilvärdekedjan.

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  • 24.
    Brännvall, Rickard
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Forsgren, Henrik
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Linge, Helena
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    HEIDA: Software Examples for Rapid Introduction of Homomorphic Encryption for Privacy Preservation of Health Data2023In: Studies in health technology and informatics, Vol. 302, p. 267-271Article in journal (Refereed)
    Abstract [en]

    Adequate privacy protection is crucial for implementing modern AI algorithms in medicine. With Fully Homomorphic Encryption (FHE), a party without access to the secret key can perform calculations and advanced analytics on encrypted data without taking part of either the input data or the results. FHE can therefore work as an enabler for situations where computations are carried out by parties that are denied plain text access to sensitive data. It is a scenario often found with digital services that process personal health-related data or medical data originating from a healthcare provider, for example, when the service is delivered by a third-party service provider located in the cloud. There are practical challenges to be aware of when working with FHE. The current work aims to improve accessibility and reduce barriers to entry by providing code examples and recommendations to aid developers working with health data in developing FHE-based applications. HEIDA is available on the GitHub repository: https://github.com/rickardbrannvall/HEIDA.

  • 25.
    Brännvall, Rickard
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Forsgren, Henrik
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Linge, Helena
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Santini, Marina
    RISE Research Institutes of Sweden, Digital Systems, Prototyping Society.
    Salehi, Alireza
    RISE Research Institutes of Sweden, Digital Systems, Prototyping Society.
    Rahimian, Fatemeh
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Homomorphic encryption enables private data sharing for digital health: Winning entry to the Vinnova innovation competition Vinter 2021-222022In: 34th Workshop of the Swedish Artificial Intelligence Society, SAIS 2022, Institute of Electrical and Electronics Engineers Inc. , 2022Conference paper (Refereed)
    Abstract [en]

    People living with type 1 diabetes often use several apps and devices that help them collect and analyse data for a better monitoring and management of their disease. When such health related data is analysed in the cloud, one must always carefully consider privacy protection and adhere to laws regulating the use of personal data. In this paper we present our experience at the pilot Vinter competition 2021-22 organised by Vinnova. The competition focused on digital services that handle sensitive diabetes related data. The architecture that we proposed for the competition is discussed in the context of a hypothetical cloud-based service that calculates diabetes self-care metrics under strong privacy preservation. It is based on Fully Homomorphic Encryption (FHE)-a technology that makes computation on encrypted data possible. Our solution promotes safe key management and data life-cycle control. Our benchmarking experiment demonstrates execution times that scale well for the implementation of personalised health services. We argue that this technology has great potentials for AI-based health applications and opens up new markets for third-party providers of such services, and will ultimately promote patient health and a trustworthy digital society.

  • 26.
    Brännvall, Rickard
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Luleå University of Technology, Sweden.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Sandin, Fredrik
    Luleå University of Technology, Sweden.
    Modular and Transferable Machine Learning for Heat Management and Reuse in Edge Data Centers2023In: Energies, E-ISSN 1996-1073, Vol. 16, no 5, article id 2255Article in journal (Refereed)
    Abstract [en]

    This study investigates the use of transfer learning and modular design for adapting a pretrained model to optimize energy efficiency and heat reuse in edge data centers while meeting local conditions, such as alternative heat management and hardware configurations. A Physics-Informed Data-Driven Recurrent Neural Network (PIDD RNN) is trained on a small scale-model experiment of a six-server data center to control cooling fans and maintain the exhaust chamber temperature within safe limits. The model features a hierarchical regularizing structure that reduces the degrees of freedom by connecting parameters for related modules in the system. With a RMSE value of 1.69, the PIDD RNN outperforms both a conventional RNN (RMSE: 3.18), and a State Space Model (RMSE: 2.66). We investigate how this design facilitates transfer learning when the model is fine-tuned over a few epochs to small dataset from a second set-up with a server located in a wind tunnel. The transferred model outperforms a model trained from scratch over hundreds of epochs.

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  • 27.
    Brännvall, Rickard
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Lulea University of Technology, Sweden.
    Mattsson, Louise
    RISE Research Institutes of Sweden.
    Lundmark, Erik
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Vesterlund, Mattias
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Data center excess heat recovery: A case study of apple drying2020In: ECOS 2020 - Proceedings of the 33rd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2020 Local Organizing Committee , 2020, p. 2165-2174Conference paper (Refereed)
    Abstract [en]

    Finding synergies between heat producing and heat consuming actors in an economy provides opportunity for more efficient energy utilization and reduction of overall power consumption. We propose to use low-grade heat recovered from data centers directly in food processing industries, for example for the drying of fruit and berries. This study analyses how the heat output of industrial IT-load on servers can dry apples in a small-scale experimental set up. To keep the temperatures of the server exhaust airflow near a desired set-point we use a model predictive controller (MPC) re-purposed to the drying experiment set-up from a previous work that used machine learning models for cluster thermal management. Thus, conditions with for example 37 C for 8 hours drying can be obtained with results very similar to conventional drying of apples. The proposed solution increases the value output of the electricity used in a data center by capturing and using the excess heat that would otherwise be exhausted. The results from our experiments show that drying foods with excess heat from data center is possible with potential of strengthening the food processing industry and contribute to food self-sufficiency in northern Sweden.

  • 28.
    Brännvall, Rickard
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Lulea University of Technology, Sweden.
    Mattsson, Louise
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Lundmark, Erik
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Vesterlund, Mattias
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Data center excess heat recovery: A case study of apple drying2020In: ECOS 2020 - Proceedings of the 33rd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, ECOS 2020 Local Organizing Committee , 2020, p. 2165-2174Conference paper (Refereed)
    Abstract [en]

    Finding synergies between heat producing and heat consuming actors in an economy provides opportunity for more efficient energy utilization and reduction of overall power consumption. We propose to use low-grade heat recovered from data centers directly in food processing industries, for example for the drying of fruit and berries. This study analyses how the heat output of industrial IT-load on servers can dry apples in a small-scale experimental set up. To keep the temperatures of the server exhaust airflow near a desired set-point we use a model predictive controller (MPC) re-purposed to the drying experiment set-up from a previous work that used machine learning models for cluster thermal management. Thus, conditions with for example 37 C for 8 hours drying can be obtained with results very similar to conventional drying of apples. The proposed solution increases the value output of the electricity used in a data center by capturing and using the excess heat that would otherwise be exhausted. The results from our experiments show that drying foods with excess heat from data center is possible with potential of strengthening the food processing industry and contribute to food self-sufficiency in northern Sweden. 

  • 29.
    Brännvall, Rickard
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Sarkinen, Jeffrey
    RISE Research Institutes of Sweden.
    Svartholm, Joar
    RISE Research Institutes of Sweden.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Summers, Jon
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Digital Twin for Tuning of Server Fan Controllers2019In: 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), 2019, p. 1425-1428Conference paper (Refereed)
    Abstract [en]

    Cooling of IT equipment consumes a large proportion of a modern data centre’s energy budget and is therefore an important target for optimal control. This study analyses a scaled down system of six servers with cooling fans by implementing a minimal data driven time-series model in TensorFlow/Keras, a modern software package popular for deep learning. The model is inspired by the physical laws of heat exchange, but with all parameters obtained by optimisation. It is encoded as a customised Recurrent Neural Network and exposed to the time-series data via n-step Prediction Error Minimisation (PEM). The thus obtained Digital Twin of the physical system is then used directly to construct a Model Predictive Control (MPC) type regulator that executes in real time. The MPC is then compared in simulation with a self-tuning PID controller that adjust its parameters on-line by gradient descent.

  • 30.
    Brännvall, Rickard
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Luleå University of Technology, Sweden.
    Siltala, Mikko
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Sarkinen, Jeffrey
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Vesterlund, Mattias
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Summers, Jon
    EDGE: Microgrid Data Center with Mixed Energy Storage2020In: e-Energy 2020 - Proceedings of the 11th ACM International Conference on Future Energy Systems, Association for Computing Machinery, Inc , 2020, p. 466-473Conference paper (Refereed)
    Abstract [en]

    Low latency requirements are expected to increase with 5G telecommunications driving data and compute to EDGE data centers located in cities near to end users. This article presents a testbed for such data centers that has been built at RISE ICE Datacenter in northern Sweden in order to perform full stack experiments on load balancing, cooling, micro-grid interactions and the use of renewable energy sources. This system is described with details on both hardware components and software implementations used for data collection and control. A use case for off-grid operation is presented to demonstrate how the test lab can be used for experiments on edge data center design, control and autonomous operation. © 2020 Author.

  • 31.
    Brännvall, Rickard
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Luleå University of Technology, Sweden.
    Stark, Tina
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Eriksson, Mats
    Arctos Labs Scandinvia AB, Sweden.
    Summers, Jon
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Cost Optimization by Energy Aware Workload Placement for the Edge Cloud Continuum2022Report (Other academic)
    Abstract [en]

    This report investigates the problem of where to place computation workload in an edge-cloud network topology considering the trade-off between the location specific cost of computation and data communication.

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  • 32.
    Brännvall, Rickard
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Stark, Tina
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Eriksson, Mats
    Arctos Labs Scandinavia AB, Sweden.
    Summers, Jon
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Cost Optimization for the Edge-Cloud Continuum by Energy-Aware Workload Placement2023In: Companion Proceedings of the 14th ACM International Conference on Future Energy Systems, Association for Computing Machinery , 2023, p. 79-84Conference paper (Refereed)
    Abstract [en]

    This article investigates the problem of where to place the computation workload in an edge-cloud network topology considering the trade-off between the location-specific cost of computation and data communication. For this purpose, a Monte Carlo simulation model is defined that accounts for different workload types, their distribution across time and location, as well as correlation structure. Results confirm and quantify the intuition that optimization can be achieved by distributing a part of cloud computation to make efficient use of resources in an edge data center network, with operational energy savings of 4–6% and up to 50% reduction in its claim for cloud capacity.

  • 33.
    Brännvall, Rickard
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Lulea University of Technology, Sweden.
    Öhman, J.
    Lulea University of Technology, Sweden.
    Kovacs, G.
    Lulea University of Technology, Sweden.
    Liwicki, M.
    Lulea University of Technology, Sweden.
    Cross-encoded meta embedding towards transfer learning2020In: ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN (i6doc.com) , 2020, p. 631-636Conference paper (Refereed)
    Abstract [en]

    In this paper we generate word meta-embeddings from already existing embeddings using cross-encoding. Previous approaches can only work with words that exist in each source embedding, while the architecture presented here drops this requirement. We demonstrate the method using two pre-trained embeddings, namely GloVE and FastText. Furthermore, we propose additional improvements to the training process of the metaembedding. Results on six standard tests for word similarity show that the meta-embedding trained outperforms the original embeddings. Moreover, this performance can be further increased with the proposed improvements, resulting in a competitive performance with those reported earlier.

  • 34.
    Bybee, Connor
    et al.
    University of California, USA.
    Kleyko, Denis
    RISE Research Institutes of Sweden, Digital Systems, Data Science. University of California, USA.
    Nikonov, Dmitri E
    Intel, USA.
    Khosrowshahi, A.
    University of California, USA; Intel, USA.
    Olshausen, B. A.
    University of California, USA.
    Sommer, F. T.
    University of California, USA; Intel, USA.
    Efficient optimization with higher-order ising machines2023In: Nature Communications, E-ISSN 2041-1723, Vol. 14, article id 6033Article in journal (Refereed)
    Abstract [en]

    A prominent approach to solving combinatorial optimization problems on parallel hardware is Ising machines, i.e., hardware implementations of networks of interacting binary spin variables. Most Ising machines leverage second-order interactions although important classes of optimization problems, such as satisfiability problems, map more seamlessly to Ising networks with higher-order interactions. Here, we demonstrate that higher-order Ising machines can solve satisfiability problems more resource-efficiently in terms of the number of spin variables and their connections when compared to traditional second-order Ising machines. Further, our results show on a benchmark dataset of Boolean k-satisfiability problems that higher-order Ising machines implemented with coupled oscillators rapidly find solutions that are better than second-order Ising machines, thus, improving the current state-of-the-art for Ising machines. 

  • 35.
    Carbone, Paris
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Fragkoulis, Marios
    Delft University of Technology, Netherlands.
    Kalavri, Vasiliki
    Boston University, USA.
    Katsifodimos, Asterios
    Delft University of Technology, Netherlands.
    Beyond Analytics: The Evolution of Stream Processing Systems2020In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Association for Computing Machinery , 2020, p. 2651-2658Conference paper (Refereed)
    Abstract [en]

    Stream processing has been an active research field for more than 20 years, but it is now witnessing its prime time due to recent successful efforts by the research community and numerous worldwide open-source communities. The goal of this tutorial is threefold. First, we aim to review and highlight noteworthy past research findings, which were largely ignored until very recently. Second, we intend to underline the differences between early ('00-'10) and modern ('11-'18) streaming systems, and how those systems have evolved through the years. Most importantly, we wish to turn the attention of the database community to recent trends: streaming systems are no longer used only for classic stream processing workloads, namely window aggregates and joins. Instead, modern streaming systems are being increasingly used to deploy general event-driven applications in a scalable fashion, challenging the design decisions, architecture and intended use of existing stream processing systems. 

  • 36.
    Carignani, Gioele
    et al.
    University of Pisa, Italy.
    Righetti, Francesca
    University of Pisa, Italy.
    Vallati, Carlo
    University of Pisa, Italy.
    Tiloca, Marco
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Anastasi, Giuseppe
    University of Pisa, Italy.
    Evaluation of Feasibility and Impact of Attacks Against the 6top Protocol in 6TiSCH Networks2020In: 2020 IEEE 21st International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM), 2020, p. 68-77Conference paper (Refereed)
    Abstract [en]

    The 6TiSCH architecture has been gaining attraction as a promising solution to ensure reliability and security for communication in applications for the Industrial Internet of Things (IIoT). While many different aspects of the architecture have been investigated in literature, an in-depth analysis of the security features included in its design is still missing. In this paper, we assess the security vulnerabilities of the 6top protocol, a core component of the 6TiSCH architecture for enabling network nodes to negotiate communication resources. Our analysis highlights two possible attacks against the 6top protocol that can impair network performance and reliability in a significant manner. To prove the feasibility of the attacks in practice, we implemented both of them on the Contiki-NG Operating System and tested their effectiveness on a simple deployment with three Zolertia RE-Mote sensor nodes. Also, we carried out a set of simulations using Cooja in order to assess their impact on larger networks. Our results show that both attacks reduce reliability in the overall network and increase energy consumption of the network nodes.

  • 37.
    Carlsson, Fredrik
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Gogoulou, Evangelina
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Ylipää, Erik
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Cuba Gyllensten, Amaru
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Sahlgren, Magnus
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Semantic Re-tuning with Contrastive Tension2021Conference paper (Refereed)
    Abstract [en]

    Extracting semantically useful natural language sentence representations frompre-trained deep neural networks such as Transformers remains a challenge. Wefirst demonstrate that pre-training objectives impose a significant task bias ontothe final layers of models, with a layer-wise survey of the Semantic Textual Similarity (STS) correlations for multiple common Transformer language models. Wethen propose a new self-supervised method called Contrastive Tension (CT) tocounter such biases. CT frames the training objective as a noise-contrastive taskbetween the final layer representations of two independent models, in turn makingthe final layer representations suitable for feature extraction. Results from multiple common unsupervised and supervised STS tasks indicate that CT outperformsprevious State Of The Art (SOTA), and when combining CT with supervised datawe improve upon previous SOTA results with large margins.

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  • 38.
    Carlsson, Fredrik
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Öhman, Joey
    Liu, Fangyu
    Verlinden, Severine
    Nirve, Joakim
    RISE Research Institutes of Sweden.
    Sahlgren, Magnus
    Fine-Grained Controllable Text Generation Using Non-Residual Prompting2022In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022, p. 6837-6857Conference paper (Refereed)
    Abstract [en]

    The introduction of immensely large Causal Language Models (CLMs) has rejuvenated the interest in open-ended text generation. However, controlling the generative process for these Transformer-based models is at large an unsolved problem. Earlier work has explored either plug-and-play decoding strategies, or more powerful but blunt approaches such as prompting. There hence currently exists a trade-off between fine-grained control, and the capability for more expressive high-level instructions. To alleviate this trade-off, we propose an encoder-decoder architecture that enables intermediate text prompts at arbitrary time steps. We propose a resource-efficient method for converting a pre-trained CLM into this architecture, and demonstrate its potential on various experiments, including the novel task of contextualized word inclusion. Our method provides strong results on multiple experimental settings, proving itself to be both expressive and versatile.

  • 39.
    Carlsson, Mats
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Ceschia, Sara
    RISE Research Institutes of Sweden, Digital Systems, Data Science. University of Udine, Italy.
    Di Gaspero, Luca
    University of Udine, Italy.
    Mikkelsen, Rasmus Ørnstrup
    DTU Technical University of Denmark, Denmark.
    Schaerf, Andrea
    University of Udine, Italy.
    Stidsen, Thomas Jacob Riis
    DTU Technical University of Denmark, Denmark.
    Exact and metaheuristic methods for a real-world examination timetabling problem2023In: Journal of Scheduling, ISSN 1094-6136, E-ISSN 1099-1425Article in journal (Refereed)
    Abstract [en]

    We propose a portfolio of exact and metaheuristic methods for the rich examination timetabling problem introduced by Battistutta et al. (in: Hebrard, Musliu (eds) 17th International conference on the integration of constraint programming, artificial intelligence, and operations research (CPAIOR-2020), LNCS, vol 12296. Springer, Berlin, pp 69–81, 2020). The problem includes several real-world features that arise in Italian universities, such as examinations split into two parts, possible requirements of multiple rooms for a single examination, and unavailabilities and preferences for periods and rooms. We developed a CP model encoded in the MiniZinc modeling language and solved it with Gecode, as well as two MIP models solved with Gurobi. The first MIP model is encoded natively and the second one again in MiniZinc. Finally, we extended the metaheuristic method based on simulated annealing of Battistutta et al. by introducing a new neighborhood relation. We compare the different techniques on the real-world instances provided by Battistutta et al., which have been slightly refined by correcting some semantic issues. Finally, we developed a solution checker that is publicly available, together with all instances and solutions, for inspection and future comparisons.

  • 40.
    Collet, Mathiou
    et al.
    Simula Research Laboratory, Norway.
    Gotlieb, Arnaud
    Simula Research Laboratory, Norway.
    Lazaar, Nadjib
    University of Montpellier, France.
    Carlsson, Mats
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Marijan, Dusica
    Simula Research Laboratory, Norway.
    Mossige, Morten
    ABB Robotics, Norway.
    RobTest: A CP Approach to Generate Maximal Test Trajectories for Industrial Robots2020In: 26th International Conference on Principles and Practice of Constraint Programming, CP 2020, Springer Science and Business Media Deutschland GmbH , 2020, p. 707-723Conference paper (Refereed)
    Abstract [en]

    Developing industrial robots which are safe, performant, robust and reliable over time is challenging, because their embedded distributed software system involves complex motions with force and torque control and anti-collision surveillance processes. Generating test trajectories which increase the chance to uncover potential failures or downtime is thus crucial to verify the reliability and performance of the robot before delivering it to its final users. Currently, these trajectories are manually created by test engineers, something that renders the process error-prone and time-consuming. In this paper, we present RobTest, a Constraint Programming approach for generating automatically maximal test trajectories for serial industrial robots. RobTest sequentially calls two constraint solvers: a solver over continuous domains to determine the reachability between configurations of the robot’s 3D-space, and a solver over finite domains to generate maximal-load test trajectories among a set of input points and obstacles of the 3D-space. RobTest is developed at ABB Robotics, a large robot manufacturing company, together with test engineers, who are preparing it for integration within the continuous testing process of the robots product-line. This paper reports on initial experimental results with three distinct solvers, namely Gecode, SICStus and Chuffed, where RobTest, has been shown to return near-optimal solutions for trajectories encounting for more than 80 input points and 60 obstacles in less than 5 min.

  • 41.
    Corcoran, D.
    et al.
    KTH Royal Institute of Technology, Sweden; Ericsson AB, Sweden; Software and Computer Systems, Sweden.
    Kreuger, Per
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Boman, M.
    KTH Royal Institute of Technology, Sweden.
    A Sample Efficient Multi-Agent Approach to Continuous Reinforcement Learning2022In: Proceedings of the 2022 18th International Conference of Network and Service Management: Intelligent Management of Disruptive Network Technologies and Services, CNSM 2022, Institute of Electrical and Electronics Engineers Inc. , 2022, p. 338-344Conference paper (Refereed)
    Abstract [en]

    As design, deployment and operation complexity increase in mobile systems, adaptive self-learning techniques have become essential enablers in mitigation and control of the complexity problem. Artificial intelligence and, in particular, reinforcement learning has shown great potential in learning complex tasks through observations. The majority of ongoing reinforcement learning research activities focus on single-Agent problem settings with an assumption of accessibility to a globally observable state and action space. In many real-world settings, such as LTE or 5G, decision making is distributed and there is often only local accessibility to the state space. In such settings, multi-Agent learning may be preferable, with the added challenge of ensuring that all agents collaboratively work towards achieving a common goal. We present a novel cooperative and distributed actor-critic multi-Agent reinforcement learning algorithm. We claim the approach is sample efficient, both in terms of selecting observation samples and in terms of assignment of credit between subsets of collaborating agents. 

  • 42.
    Corcoran, Diarmuid
    et al.
    KTH Royal Institute of Technology, Sweden; Ericsson, Sweden.
    Kreuger, Per
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Schulte, Christian
    KTH Royal Institute of Technology, Sweden.
    Efficient Real-Time Traffic Generation for 5G RAN2020In: Proceedings of IEEE/IFIP Network Operations and Management Symposium 2020: Management in the Age of Softwarization and Artificial Intelligence, NOMS 2020, Institute of Electrical and Electronics Engineers Inc. , 2020Conference paper (Refereed)
    Abstract [en]

    Modern telecommunication and mobile networks are increasingly complex from a resource management perspective, with diverse combinations of software and infrastructure elements that need to be configured and tuned for efficient operation with high quality of service. Increased real-time automation at all levels and time-frames is a critical tool in controlling this complexity. A key component in automation is practical and accurate simulation methods that can be used in live traffic scenarios. This paper introduces a new method with supporting algorithms for sampling key parameters from live or recorded traffic which can be used to generate large volumes of synthetic traffic with very similar rate distributions and temporal characteristics. Multiple spatial renewal processes are used to generate fractional Gaussian noise, which is scaled and transformed into a log-normal rate distribution with discrete arrival events, fitted to the properties observed in given recorded traces. This approach works well for modelling large user aggregates but is especially useful for medium sized and relatively small aggregates, where existing methods struggle to reproduce the most important properties of recorded traces. The technique is demonstrated through experimental comparisons with data collected from an operational LTE network to be highly useful in supporting self-learning and automation algorithms which can ultimately reduce complexity, increase energy efficiency, and reduce total network operation costs. 

  • 43.
    Cotton, Kelsey
    et al.
    KTH Royal Institute of Technology, Sweden.
    Afsar, Ozgun Kilic
    KTH Royal Institute of Technology, Sweden; MIT Media Lab, USA.
    Luft, Yoav
    KTH Royal Institute of Technology, Sweden.
    Syal, Priyanka
    KTH Royal Institute of Technology, Sweden.
    Ben Abdesslem, Fehmi
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    SymbioSinging: Robotically Transposing Singing Experience across Singing and Non-Singing Bodies2021In: Creativity and Cognition, Association for Computing Machinery , 2021, article id 52Conference paper (Refereed)
    Abstract [en]

    In this paper we present our late-breaking work in leveraging a soft robotic fiber-based wearable system for the transposition of somatic knowledge and experience within the context of singing. We examine how the transposition of the physical nuances of singing from one body to another, or multiple other bodies, is possible by engaging with a soma design process. We share our findings in the context of experience transposition, resulting in a preliminary prototype: a pneumatically controlled soft robotic garment—called ADA (short for air-driven actuator) for re-enacting felt experiences of singing onto the human body. We contribute with 1) our initial findings in transposing singing experiences between and across bodies, and 2) a preliminary wearable robotic garment to mediate intersomatic experiences of singing.

  • 44.
    Cáceres, Cristina
    et al.
    Luleå University of Technology, Sweden.
    Törnroth, Suzanna
    Luleå University of Technology, Sweden.
    Vesterlund, Mattias
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Johansson, Andreas
    Luleå University of Technology, Sweden.
    Sandberg, Marcus
    Luleå University of Technology, Sweden.
    Data-Center Farming: Exploring the Potential of Industrial Symbiosis in a Subarctic Region2022In: Sustainability, E-ISSN 2071-1050, Vol. 14, no 5, article id 2774Article in journal (Refereed)
    Abstract [en]

    As our world becomes increasingly digitalized, data centers as operational bases for these technologies lead to a consequent increased release of excess heat into the surrounding environment. This paper studies the challenges and opportunities of industrial symbiosis between data centers’ excess heat and greenhouse farming, specifically utilizing the north of Sweden as a case study region. The region was selected in a bid to tackle the urgent urban issue of self-sufficiency in local food production. A synergetic approach towards engaging stakeholders from different sectors is presented through a mix of qualitative and quantitative methods to facilitate resilient data-center-enabled food production. The paper delivers on possible future solutions on implementing resource efficiency in subarctic regions. © 2022 by the authors. 

  • 45.
    Davari, Narjes
    et al.
    INESC TEC, Portugal.
    Pashami, Sepideh
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Halmstad University, Sweden.
    Veloso, Bruno
    INESC TEC, Portugal; University Portucalense, Portugal.
    Nowaczyk, Stawomir
    Halmstad University, Sweden.
    Fan, Yuantao
    Halmstad University, Sweden.
    Pereira, Pedro Mota
    Metro of Porto, Portugal.
    Ribeiro, Rita
    INESC TEC, Portugal; University of Porto, Portugal.
    Gama, Joao
    INESC TEC, Portugal; University of Porto, Portugal.
    A Fault Detection Framework Based on LSTM Autoencoder: A Case Study for Volvo Bus Data Set2022In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Volume 13205 LNCS, Pages 39 - 522022, Springer Science and Business Media Deutschland GmbH , 2022, p. 39-52Conference paper (Refereed)
    Abstract [en]

    This study applies a data-driven anomaly detection framework based on a Long Short-Term Memory (LSTM) autoencoder network for several subsystems of a public transport bus. The proposed framework efficiently detects abnormal data, significantly reducing the false alarm rate compared to available alternatives. Using historical repair records, we demonstrate how detection of abnormal sequences in the signals can be used for predicting equipment failures. The deviations from normal operation patterns are detected by analysing the data collected from several on-board sensors (e.g., wet tank air pressure, engine speed, engine load) installed on the bus. The performance of LSTM autoencoder (LSTM-AE) is compared against the multi-layer autoencoder (mlAE) network in the same anomaly detection framework. The experimental results show that the performance indicators of the LSTM-AE network, in terms of F1 Score, Recall, and Precision, are better than those of the mlAE network. © 2022, The Author(s)

  • 46.
    Dhole, Kaustubh
    et al.
    Emory University, USA; Amelia R&D, USA.
    Kleyko, Denis
    RISE Research Institutes of Sweden, Digital Systems, Data Science. University of California, USA.
    Zhang, Yue
    Westlake Institute for Advanced Study, USA.
    NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation2023In: NEJLT Northern European Journal of Language Technology, ISSN 2000-1533, Vol. 9, no 1, p. 1-41Article in journal (Refereed)
    Abstract [en]

    Data augmentation is an important method for evaluating the robustness of and enhancing the diversity of training datafor natural language processing (NLP) models. In this paper, we present NL-Augmenter, a new participatory Python-based naturallanguage (NL) augmentation framework which supports the creation of transformations (modifications to the data) and filters(data splits according to specific features). We describe the framework and an initial set of117transformations and23filters for avariety of NL tasks annotated with noisy descriptive tags. The transformations incorporate noise, intentional and accidental humanmistakes, socio-linguistic variation, semantically-valid style, syntax changes, as well as artificial constructs that are unambiguousto humans. We demonstrate the efficacy of NL-Augmenter by using its transformations to analyze the robustness of popularlanguage models. We find different models to be differently challenged on different tasks, with quasi-systematic score decreases.The infrastructure, datacards, and robustness evaluation results are publicly available onGitHubfor the benefit of researchersworking on paraphrase generation, robustness analysis, and low-resource NLP.

  • 47.
    Dimitriou, Tassos
    et al.
    Kuwait University, Kuwait.
    Michalas, Antonis
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Tampere University, Finland.
    Incentivizing Participation in Crowd-Sensing Applications Through Fair and Private Bitcoin Rewards2022In: IEEE Access, E-ISSN 2169-3536, Vol. 10, p. 129004-129018Article in journal (Refereed)
    Abstract [en]

    In this work we develop a rewarding framework that can be used to enhance existing crowd-sensing applications. Although a core requirement of such systems is user engagement, people may be reluctant to participate because sensitive information about them may be leaked or inferred from submitted data. The use of monetary rewards can help incentivize participation, thereby increasing not only the amount but also the quality of sensed data. Our framework allows users to submit data and obtain Bitcoin payments in a privacy-preserving manner, preventing curious providers from linking the data or the payments back to the user. At the same time, it prevents malicious user behavior such as double-redeeming attempts, where a user tries to obtain rewards for multiple submissions of the same data. More importantly, it ensures the fairness of the exchange in a completely trustless manner; by relying on the Blockchain, the trust placed on third parties in traditional fair exchange protocols is eliminated. Finally, our system is highly efficient as most of the protocol steps do not utilize the Blockchain network. When they do, only the simplest of Blockchain transactions are used as opposed to prior works that are based on the use of more complex smart contracts.

  • 48.
    Ding, Yiyu
    et al.
    NTNU, Norway.
    Timoudas, Thomas Ohlson
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Wang, Qian
    KTH Royal Institute of Technology, Sweden; Uponor AB,Sweden.
    Chen, Shuqin
    Zhejiang University, China.
    Brattebø, Helge
    NTNU, Norway.
    Nord, Natasa
    NTNU, Norway.
    A study on data-driven hybrid heating load prediction methods in low-temperature district heating: An example for nursing homes in Nordic countries2022In: Energy Conversion and Management, ISSN 0196-8904, E-ISSN 1879-2227, Vol. 269, article id 116163Article in journal (Refereed)
    Abstract [en]

    In the face of green energy initiatives and progressively increasing shares of more energy-efficient buildings, there is a pressing need to transform district heating towards low-temperature district heating. The substantially lowered supply temperature of low-temperature district heating broadens the opportunities and challenges to integrate distributed renewable energy, which requires enhancement on intelligent heating load prediction. Meanwhile, to fulfill the temperature requirements for domestic hot water and space heating, separate energy conversion units on user-side, such as building-sized boosting heat pumps shall be implemented to upgrade the temperature level of the low-temperature district heating network. This study conducted hybrid heating load prediction methods with long-term and short-term prediction, and the main work consisted of four steps: (1) acquisition and processing of district heating data of 20 district heating supplied nursing homes in the Nordic climate (2016–2019); (2) long-term district heating load prediction through linear regression, energy signature curve in hourly resolution, providing an overall view and boundary conditions for the unit sizing; (3) short-term district heating load prediction through two Artificial Neural Network models, f72 and g120, with different prediction input parameters; (4) evaluation of the predicted load profiles based on the measured data. Although the three prediction models met the quality criteria, it was found that including the historical hourly heating loads as the input to the forecasting model enhanced the prediction quality, especially for the peak load and low-mild heating season. Furthermore, a possible application of the heating load profiles was proposed by integrating two building-sized heat pumps in low-temperature district heating, which may be a promising heat supply method in low-temperature district heating. © 2022 The Authors

  • 49.
    Dong, Guojun
    et al.
    University of Copenhagen, Denmark.
    Bate, Andrew
    GSK, United Kingdom; London School of Hygiene and Tropical Medicine, United Kingdom.
    Haguinet, François
    GSK, United Kingdom.
    Westman, Gabriel
    Uppsala University, Sweden.
    Dürlich, Luise
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Uppsala University, Sweden.
    Hviid, Anders
    University of Copenhagen, Denmark; Statens Serum Institut, Denmark.
    Sessa, Maurizio
    University of Copenhagen, Denmark.
    Optimizing Signal Management in a Vaccine Adverse Event Reporting System: A Proof-of-Concept with COVID-19 Vaccines Using Signs, Symptoms, and Natural Language Processing2023In: Drug Safety, ISSN 0114-5916, E-ISSN 1179-1942Article in journal (Refereed)
    Abstract [en]

    Introduction: The Vaccine Adverse Event Reporting System (VAERS) has already been challenged by an extreme increase in the number of individual case safety reports (ICSRs) after the market introduction of coronavirus disease 2019 (COVID-19) vaccines. Evidence from scientific literature suggests that when there is an extreme increase in the number of ICSRs recorded in spontaneous reporting databases (such as the VAERS), an accompanying increase in the number of disproportionality signals (sometimes referred to as ‘statistical alerts’) generated is expected. Objectives: The objective of this study was to develop a natural language processing (NLP)-based approach to optimize signal management by excluding disproportionality signals related to listed adverse events following immunization (AEFIs). COVID-19 vaccines were used as a proof-of-concept. Methods: The VAERS was used as a data source, and the Finding Associated Concepts with Text Analysis (FACTA+) was used to extract signs and symptoms of listed AEFIs from MEDLINE for COVID-19 vaccines. Disproportionality analyses were conducted according to guidelines and recommendations provided by the US Centers for Disease Control and Prevention. By using signs and symptoms of listed AEFIs, we computed the proportion of disproportionality signals dismissed for COVID-19 vaccines using this approach. Nine NLP techniques, including Generative Pre-Trained Transformer 3.5 (GPT-3.5), were used to automatically retrieve Medical Dictionary for Regulatory Activities Preferred Terms (MedDRA PTs) from signs and symptoms extracted from FACTA+. Results: Overall, 17% of disproportionality signals for COVID-19 vaccines were dismissed as they reported signs and symptoms of listed AEFIs. Eight of nine NLP techniques used to automatically retrieve MedDRA PTs from signs and symptoms extracted from FACTA+ showed suboptimal performance. GPT-3.5 achieved an accuracy of 78% in correctly assigning MedDRA PTs. Conclusion: Our approach reduced the need for manual exclusion of disproportionality signals related to listed AEFIs and may lead to better optimization of time and resources in signal management. © 2023, The Author(s).

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  • 50.
    Dwibedi, C.
    et al.
    University of Gothenburg,.
    Mellergård, E.
    Lund University, Sweden.
    Gyllensten, Amaru Cubac
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Nilsson, K.
    Swedish Institute for Health Economics, Sweden.
    Axelsson, A. S.
    University of Gothenburg,.
    Bäckman, M.
    Lund University, Sweden.
    Sahlgren, Magnus
    RISE Research Institutes of Sweden.
    Friend, S. H.
    University of Oxford, UK.
    Persson, S.
    Swedish Institute for Health Economics, Sweden.
    Franzén, S.
    RegisterCentrum Västra Götaland, Sweden; University of Gothenburg, Sweden.
    Abrahamsson, B.
    University of Gothenburg, Sweden.
    Carlsson, K. S.
    Swedish Institute for Health Economics, Sweden.
    Rosengren, A. H.
    University of Gothenburg, Sweden; Lund University, Sweden.
    Effect of self-managed lifestyle treatment on glycemic control in patients with type 2 diabetes2022In: npj Digital Medicine, ISSN 2398-6352, Vol. 5, no 1, article id 60Article in journal (Refereed)
    Abstract [en]

    The lack of effective, scalable solutions for lifestyle treatment is a global clinical problem, causing severe morbidity and mortality. We developed a method for lifestyle treatment that promotes self-reflection and iterative behavioral change, provided as a digital tool, and evaluated its effect in 370 patients with type 2 diabetes (ClinicalTrials.gov identifier: NCT04691973). Users of the tool had reduced blood glucose, both compared with randomized and matched controls (involving 158 and 204 users, respectively), as well as improved systolic blood pressure, body weight and insulin resistance. The improvement was sustained during the entire follow-up (average 730 days). A pathophysiological subgroup of obese insulin-resistant individuals had a pronounced glycemic response, enabling identification of those who would benefit in particular from lifestyle treatment. Natural language processing showed that the metabolic improvement was coupled with the self-reflective element of the tool. The treatment is cost-saving because of improved risk factor control for cardiovascular complications. The findings open an avenue for self-managed lifestyle treatment with long-term metabolic efficacy that is cost-saving and can reach large numbers of people. © 2022, The Author(s).

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