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Ben Abdesslem, FehmiORCID iD iconorcid.org/0000-0001-7866-143x
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Publications (10 of 24) Show all publications
Kilic Afsar, O., Luft, Y., Cotton, K., Stepanova, E., Núñez-Pacheco, C., Kleinberger, R., . . . Höök, K. (2023). Corseto: A Kinesthetic Garment for Designing, Composing for, and Experiencing an Intersubjective Haptic Voice. In: Conference on Human Factors in Computing Systems - Proceedings: . Paper presented at 2023 CHI Conference on Human Factors in Computing Systems, CHI 2023, 23 April 2023 through 28 April 2023. Association for Computing Machinery
Open this publication in new window or tab >>Corseto: A Kinesthetic Garment for Designing, Composing for, and Experiencing an Intersubjective Haptic Voice
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2023 (English)In: Conference on Human Factors in Computing Systems - Proceedings, Association for Computing Machinery , 2023Conference paper, Published paper (Refereed)
Abstract [en]

We present a novel intercorporeal experience - an intersubjective haptic voice. Through an autobiographical design inquiry, based on singing techniques from the classical opera tradition, we created Corsetto, a kinesthetic garment for transferring somatic reminiscents of vocal experience from an expert singer to a listener. We then composed haptic gestures enacted in the Corsetto, emulating upper-body movements of the live singer performing a piece by Morton Feldman named Three Voices. The gestures in the Corsetto added a haptics-based 'fourth voice' to the immersive opera performance. Finally, we invited audiences who were asked to wear Corsetto during live performances. Afterwards they engaged in micro-phenomenological interviews. The analysis revealed how the Corsetto managed to bridge inner and outer bodily sensations, creating a feeling of a shared intercorporeal experience, dissolving boundaries between listener, singer and performance. We propose that 'intersubjective haptics' can be a generative medium not only for singing performances, but other possible intersubjective experiences. © 2023 Owner/Author.

Place, publisher, year, edition, pages
Association for Computing Machinery, 2023
Keywords
haptics, machine learning, micro-phenomenology, Robotic textiles, shape changing interfaces, somaesthetic interaction design, voice, Inquiry-based, Interaction design, Kinesthetics, Machine-learning, Performance, Robotic textile, Shape changing interface, Somesthetic interaction design
National Category
Human Computer Interaction
Identifiers
urn:nbn:se:ri:diva-65359 (URN)10.1145/3544548.3581294 (DOI)2-s2.0-85160021179 (Scopus ID)9781450394215 (ISBN)
Conference
2023 CHI Conference on Human Factors in Computing Systems, CHI 2023, 23 April 2023 through 28 April 2023
Note

Funding details: Social Sciences and Humanities Research Council of Canada, SSHRC; Funding details: Stiftelsen för Strategisk Forskning, SSF, CHI19-0034; Funding details: Vetenskapsrådet, VR; Funding text 1: This work has been supported by Hardware for Energy Efcient Bodynets funded by the Swedish Foundation for Strategic Research project CHI19-0034. The work was also partially supported by Swedish Research Council project 2021-04659 Validating Soma Design and Social Sciences and Humanities Research Council of Canada.

Available from: 2023-06-15 Created: 2023-06-15 Last updated: 2023-06-15Bibliographically approved
Wallert, J., Boberg, J., Kaldo, V., Mataix-Cols, D., Flygare, O., Crowley, J. J., . . . Rück, C. (2022). Predicting remission after internet-delivered psychotherapy in patients with depression using machine learning and multi-modal data. Translational Psychiatry, 12(1), Article ID 357.
Open this publication in new window or tab >>Predicting remission after internet-delivered psychotherapy in patients with depression using machine learning and multi-modal data
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2022 (English)In: Translational Psychiatry, E-ISSN 2158-3188, Vol. 12, no 1, article id 357Article in journal (Refereed) Published
Abstract [en]

This study applied supervised machine learning with multi-modal data to predict remission of major depressive disorder (MDD) after psychotherapy. Genotyped adult patients (n = 894, 65.5% women, age 18–75 years) diagnosed with mild-to-moderate MDD and treated with guided Internet-based Cognitive Behaviour Therapy (ICBT) at the Internet Psychiatry Clinic in Stockholm were included (2008–2016). Predictor types were demographic, clinical, process (e.g., time to complete online questionnaires), and genetic (polygenic risk scores). Outcome was remission status post ICBT (cut-off ≤10 on MADRS-S). Data were split into train (60%) and validation (40%) given ICBT start date. Predictor selection employed human expertise followed by recursive feature elimination. Model derivation was internally validated through cross-validation. The final random forest model was externally validated against a (i) null, (ii) logit, (iii) XGBoost, and (iv) blended meta-ensemble model on the hold-out validation set. Feature selection retained 45 predictors representing all four predictor types. With unseen validation data, the final random forest model proved reasonably accurate at classifying post ICBT remission (Accuracy 0.656 [0.604, 0.705], P vs null model = 0.004; AUC 0.687 [0.631, 0.743]), slightly better vs logit (bootstrap D = 1.730, P = 0.084) but not vs XGBoost (D = 0.463, P = 0.643). Transparency analysis showed model usage of all predictor types at both the group and individual patient level. A new, multi-modal classifier for predicting MDD remission status after ICBT treatment in routine psychiatric care was derived and empirically validated. The multi-modal approach to predicting remission may inform tailored treatment, and deserves further investigation to attain clinical usefulness. © 2022, The Author(s).

Place, publisher, year, edition, pages
Springer Nature, 2022
National Category
Neurology
Identifiers
urn:nbn:se:ri:diva-60170 (URN)10.1038/s41398-022-02133-3 (DOI)2-s2.0-85137074379 (Scopus ID)
Note

Funding details: SLS-941192 JW; Funding details: Familjen Erling-Perssons Stiftelse, 2016-01961; Funding details: Stockholms Läns Landsting, SLL20170708; Funding details: Vetenskapsrådet, VR, 2021-06377 JW; 2018-02487 CR; Funding details: Forskningsrådet om Hälsa, Arbetsliv och Välfärd, FORTE, 2018-00221 CR; Funding details: Center for Innovative Medicine, CIMED, 954440 CR, 96328; Funding text 1: JW and CR gratefully acknowledge funding from the Söderström-König Foundation (SLS-941192 JW), FORTE (2018-00221 CR), the Swedish Research Council (2021-06377 JW; 2018-02487 CR) and the Center for innovative medicine (CIMED 96328 JW; 954440 CR). MB and VK gratefully acknowledge the Stockholm County Council (funding through the Swedish Medical Training and Research Agreement (ALF) (SLL20170708) and infrastructure via the Internet Psychiatry Clinic), the Erling-Persson Family Foundation, and the Swedish Research Council (2016-01961). MB is partially funded by the WASP (Wallenberg Autonomous Systems and Software Program). Open access funding provided by Karolinska Institute.

Available from: 2022-09-29 Created: 2022-09-29 Last updated: 2024-01-17Bibliographically approved
Rabitsch, A., Grinnemo, K. J., Brunstrom, A., Abrahamsson, H., Ben Abdesslem, F., Alfredsson, S. & Ahlgren, B. (2022). Utilizing Multi-Connectivity to Reduce Latency and Enhance Availability for Vehicle to Infrastructure Communication. IEEE Transactions on Mobile Computing, 21(1), 352-365
Open this publication in new window or tab >>Utilizing Multi-Connectivity to Reduce Latency and Enhance Availability for Vehicle to Infrastructure Communication
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2022 (English)In: IEEE Transactions on Mobile Computing, ISSN 1536-1233, E-ISSN 1558-0660, Vol. 21, no 1, p. 352-365Article in journal (Refereed) Published
Abstract [en]

Cooperative Intelligent Transport Systems (C-ITS) enable information to be shared wirelessly between vehicles and infrastructure in order to improve transport safety and efficiency. Delivering C-ITS services using existing cellular networks offers both financial and technological advantages, not least since these networks already offer many of the features needed by C-ITS, and since many vehicles on our roads are already connected to cellular networks. Still, C-ITS pose stringent requirements in terms of availability and latency on the underlying communication system; requirements that will be hard to meet for currently deployed 3G, LTE, and early-generation 5G systems. Through a series of experiments in the MONROE testbed (a cross-national, mobile broadband testbed), the present study demonstrates how cellular multi-access selection algorithms can provide close to 100% availability, and significantly reduce C-ITS transaction times. The study also proposes and evaluates a number of low-complexity, low-overhead single-access selection algorithms, and shows that it is possible to design such solutions so that they offer transaction times and availability levels that rival those of multi-access solutions.

Keywords
Cooperative intelligent transport systems (C-ITS), multi-connectivity, multi-access, cellular networks, interface selection
National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-49084 (URN)10.1109/TMC.2020.3028306 (DOI)85099742008 (Scopus ID)
Available from: 2020-10-13 Created: 2020-10-13 Last updated: 2023-05-19Bibliographically approved
Gogoulou, E., Boman, M., Ben Abdesslem, F., Isacsson, N., Kaldo, V. & Sahlgren, M. (2021). Predicting treatment outcome from patient texts: The case of internet-based cognitive behavioural therapy. In: EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference: . Paper presented at 16th Conference of the European Chapter of the Associationfor Computational Linguistics, EACL 2021, 19 April 2021 through 23 April 2021 (pp. 575-580). Association for Computational Linguistics (ACL)
Open this publication in new window or tab >>Predicting treatment outcome from patient texts: The case of internet-based cognitive behavioural therapy
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2021 (English)In: EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, Association for Computational Linguistics (ACL) , 2021, p. 575-580Conference paper, Published paper (Refereed)
Abstract [en]

We investigate the feasibility of applying standard text categorisation methods to patient text in order to predict treatment outcome in Internet-based cognitive behavioural therapy. The data set is unique in its detail and size for regular care for depression, social anxiety, and panic disorder. Our results indicate that there is a signal in the depression data, albeit a weak one. We also perform terminological and sentiment analysis, which confirm those results. 

Place, publisher, year, edition, pages
Association for Computational Linguistics (ACL), 2021
Keywords
Computational linguistics, Sentiment analysis, Data set, Internet based, Social anxieties, Treatment outcomes, Patient treatment
National Category
Applied Psychology
Identifiers
urn:nbn:se:ri:diva-53529 (URN)2-s2.0-85107290691 (Scopus ID)9781954085022 (ISBN)
Conference
16th Conference of the European Chapter of the Associationfor Computational Linguistics, EACL 2021, 19 April 2021 through 23 April 2021
Available from: 2021-06-17 Created: 2021-06-17 Last updated: 2024-05-15Bibliographically approved
Cotton, K., Afsar, O. K., Luft, Y., Syal, P. & Ben Abdesslem, F. (2021). SymbioSinging: Robotically Transposing Singing Experience across Singing and Non-Singing Bodies. In: Creativity and Cognition: . Paper presented at C&C'21: Creativity and Cognition. June 2021. Association for Computing Machinery, Article ID 52.
Open this publication in new window or tab >>SymbioSinging: Robotically Transposing Singing Experience across Singing and Non-Singing Bodies
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2021 (English)In: Creativity and Cognition, Association for Computing Machinery , 2021, article id 52Conference paper, Published 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.

Place, publisher, year, edition, pages
Association for Computing Machinery, 2021
Keywords
closed-loop control, touch, voice, intersomatic, movement-based HCI, soft actuators, soft sensors, Somaesthetic interaction design
National Category
Human Computer Interaction
Identifiers
urn:nbn:se:ri:diva-55442 (URN)10.1145/3450741.3466718 (DOI)
Conference
C&C'21: Creativity and Cognition. June 2021
Available from: 2021-07-08 Created: 2021-07-08 Last updated: 2021-07-08Bibliographically approved
Forsell, E., Isacsson, N., Blom, K., Jernelöv, S., Ben Abdesslem, F., Lindefors, N., . . . Kaldo, V. (2020). Predicting treatment failure in regular care Internet-Delivered Cognitive Behavior Therapy for depression and anxiety using only weekly symptom measures. Journal of Consulting and Clinical Psychology, 88(4), 311-321
Open this publication in new window or tab >>Predicting treatment failure in regular care Internet-Delivered Cognitive Behavior Therapy for depression and anxiety using only weekly symptom measures
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2020 (English)In: Journal of Consulting and Clinical Psychology, ISSN 0022-006X, E-ISSN 1939-2117, Vol. 88, no 4, p. 311-321Article in journal (Refereed) Published
Abstract [en]

OBJECTIVE: Therapist guided Internet-Delivered Cognitive Behavior Therapy (ICBT) is effective, but as in traditional CBT, not all patients improve, and clinicians generally fail to identify them early enough. We predict treatment failure in 12-week regular care ICBT for Depression, Panic disorder and Social anxiety disorder, using only patients' weekly symptom ratings to identify when the accuracy of predictions exceed 2 benchmarks: (a) chance, and (b) empirically derived clinician preferences for actionable predictions.

METHOD: Screening, pretreatment and weekly symptom ratings from 4310 regular care ICBT-patients from the Internet Psychiatry Clinic in Stockholm, Sweden was analyzed in a series of regression models each adding 1 more week of data. Final score was predicted in a holdout test sample, which was then categorized into Success or Failure (failure defined as the absence of both remitter and responder status). Classification analyses with Balanced Accuracy and 95% Confidence intervals was then compared to predefined benchmarks.

RESULTS: Benchmark 1 (better than chance) was reached 1 week into all treatments. Social anxiety disorder reached Benchmark 2 (> 65%) at week 5, whereas Depression and Panic Disorder reached it at week 6.

CONCLUSIONS: For depression, social anxiety and panic disorder, prediction with only patient-rated symptom scores can detect treatment failure 6 weeks into ICBT, with enough accuracy for a clinician to take action. Early identification of failing treatment attempts may be a viable way to increase the overall success rate of existing psychological treatments by providing extra clinical resources to at-risk patients, within a so-called Adaptive Treatment Strategy. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-42528 (URN)10.1037/ccp0000462 (DOI)31829635 (PubMedID)2-s2.0-85076437637 (Scopus ID)
Available from: 2020-01-10 Created: 2020-01-10 Last updated: 2021-01-13Bibliographically approved
Gong, Q., Chen, Y., Yu, X., Xu, C., Guo, Z., Xiao, Y., . . . Hui, P. (2019). Exploring the power of social hub services. World wide web (Bussum), 22(6), 2825-2852
Open this publication in new window or tab >>Exploring the power of social hub services
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2019 (English)In: World wide web (Bussum), ISSN 1386-145X, E-ISSN 1573-1413, Vol. 22, no 6, p. 2825-2852Article in journal (Refereed) Published
Abstract [en]

Given the diverse focuses of emerging online social networks (OSNs), it is common that a user has signed up on multiple OSNs. Social hub services, a.k.a., social directory services, help each user manage and exhibit her OSN accounts on one webpage. In this work, we conduct a data-driven study by crawling over one million user profiles from about.me, a representative online social hub service. Our study aims at gaining insights on cross-OSN social influence from the crawled data. We first analyze the composition of the social hub users. For each user, we collect her social accounts from her social hub webpage, and aggregate the content generated by these accounts on different OSNs to gain a comprehensive view of this user. According to our analysis, there is a high probability that a user would provide consistent information on different OSNs. We then explore the correlation between user activities on different OSNs, based on which we propose a cross-OSN social influence prediction model. With the model, we can accurately predict a user’s social influence on emerging OSNs, such as Instagram, Foursquare, and Flickr, based on her data published on well-established OSNs like Twitter.

Keywords
Machine learning, Measurement, Online social networks, Social hub services, Social influence, Learning systems, Social networking (online), Directory service, Gaining insights, High probability, On-line social networks, Online social networks (OSNs), User activity, Economic and social effects
National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-36927 (URN)10.1007/s11280-018-0633-7 (DOI)2-s2.0-85053864788 (Scopus ID)
Available from: 2018-12-28 Created: 2018-12-28 Last updated: 2020-01-23Bibliographically approved
Boman, M., Ben Abdesslem, F., Forsell, E., Gillblad, D., Görnerup, O., Isacsson, N., . . . Kaldo, V. (2019). Learning machines in Internet-delivered psychological treatment. Progress in Artificial Intelligence, 8(4), 475-485
Open this publication in new window or tab >>Learning machines in Internet-delivered psychological treatment
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2019 (English)In: Progress in Artificial Intelligence, ISSN 2192-6352, E-ISSN 2192-6360, Vol. 8, no 4, p. 475-485Article in journal (Refereed) Published
Abstract [en]

A learning machine, in the form of a gating network that governs a finite number of different machine learning methods, is described at the conceptual level with examples of concrete prediction subtasks. A historical data set with data from over 5000 patients in Internet-based psychological treatment will be used to equip healthcare staff with decision support for questions pertaining to ongoing and future cases in clinical care for depression, social anxiety, and panic disorder. The organizational knowledge graph is used to inform the weight adjustment of the gating network and for routing subtasks to the different methods employed locally for prediction. The result is an operational model for assisting therapists in their clinical work, about to be subjected to validation in a clinical trial.

Place, publisher, year, edition, pages
Springer Verlag, 2019
Keywords
Ensemble learning, Gating network, Internet-based psychological treatment, Learning machine, Machine learning, Decision support systems, Learning systems, Conceptual levels, Decision supports, Learning machines, Machine learning methods, Operational model, Organizational knowledge, Psychological treatments, Patient treatment
National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-39062 (URN)10.1007/s13748-019-00192-0 (DOI)2-s2.0-85066625908 (Scopus ID)
Available from: 2019-06-26 Created: 2019-06-26 Last updated: 2024-06-25Bibliographically approved
Qiao, Y., Si, Z., Zhang, Y., Ben Abdesslem, F., Zhang, X. & Yang, J. (2018). A hybrid Markov-based model for human mobility prediction. Neurocomputing, 278(SI), 99-109
Open this publication in new window or tab >>A hybrid Markov-based model for human mobility prediction
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2018 (English)In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 278, no SI, p. 99-109Article in journal (Refereed) Published
Abstract [en]

Human mobility behavior is far from random, and its indicators follow non-Gaussian distributions. Predicting human mobility has the potential to enhance location-based services, intelligent transportation systems, urban computing, and so forth. In this paper, we focus on improving the prediction accuracy of non-Gaussian mobility data by constructing a hybrid Markov-based model, which takes the non-Gaussian and spatio-temporal characteristics of real human mobility data into account. More specifically, we (1) estimate the order of the Markov chain predictor by adapting it to the length of frequent individual mobility patterns, instead of using a fixed order, (2) consider the time distribution of mobility patterns occurrences when calculating the transition probability for the next location, and (3) employ the prediction results of users with similar trajectories if the recent context has not been previously seen. We have conducted extensive experiments on real human trajectories collected during 21 days from 3474 individuals in an urban Long Term Evolution (LTE) network, and the results demonstrate that the proposed model for non-Gaussian mobility data can help predicting people’s future movements with more than 56% accuracy. 

Keywords
Non-Gaussian mobility dataHybrid Markov-based modelHuman mobilityMobility predictionSpatio-temporal regularity
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-33735 (URN)10.1016/j.neucom.2017.05.101 (DOI)2-s2.0-85028806274 (Scopus ID)
Available from: 2018-04-14 Created: 2018-04-14 Last updated: 2019-01-07Bibliographically approved
Abrahamsson, H., Ben Abdesslem, F., Ahlgren, B., Brunstrom, A., Marsh, I. & Björkman, M. (2018). Connected Vehicles in Cellular Networks: Multi-access versus Single-access Performance. In: : . Paper presented at 2nd Workshop on Mobile Network Measurement (MNM’18). , Article ID 8506559.
Open this publication in new window or tab >>Connected Vehicles in Cellular Networks: Multi-access versus Single-access Performance
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2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Connected vehicles can make roads traffic safer andmore efficient, but require the mobile networks to handle timecriticalapplications. Using the MONROE mobile broadbandmeasurement testbed we conduct a multi-access measurementstudy on buses. The objective is to understand what networkperformance connected vehicles can expect in today’s mobilenetworks, in terms of transaction times and availability. The goalis also to understand to what extent access to several operatorsin parallel can improve communication performance.In our measurement experiments we repeatedly transfer warningmessages from moving buses to a stationary server. Wetriplicate the messages and always perform three transactionsin parallel over three different cellular operators. This creates adataset with which we can compare the operators in an objectiveway and with which we can study the potential for multi-access.In this paper we use the triple-access dataset to evaluate singleaccessselection strategies, where one operator is chosen for eachtransaction. We show that if we have access to three operatorsand for each transaction choose the operator with best accesstechnology and best signal quality then we can significantlyimprove availability and transaction times compared to theindividual operators. The median transaction time improves with6% compared to the best single operator and with 61% comparedto the worst single operator. The 90-percentile transaction timeimproves with 23% compared to the best single operator andwith 65% compared to the worst single operator.

National Category
Communication Systems
Identifiers
urn:nbn:se:ri:diva-34311 (URN)10.23919/TMA.2018.8506559 (DOI)2-s2.0-85057241219 (Scopus ID)
Conference
2nd Workshop on Mobile Network Measurement (MNM’18)
Available from: 2018-07-30 Created: 2018-07-30 Last updated: 2023-05-19Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0001-7866-143x

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