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Ben Abdesslem, FehmiORCID iD iconorcid.org/0000-0001-7866-143x
Alternative names
Publications (10 of 20) Show all publications
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, 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: 2020-01-10Bibliographically approved
Forsell, E., Isacsson, N., Blom, K., Jernelöv, S., Ben Abdesslem, F., Lindefors, N., . . . Kaldo, V. (2019). 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
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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2019 (English)In: Journal of Consulting and Clinical Psychology, ISSN 0022-006X, E-ISSN 1939-2117Article in journal (Refereed) Epub ahead of print
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)
Available from: 2020-01-10 Created: 2020-01-10 Last updated: 2020-01-10Bibliographically 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: 2019-01-07Bibliographically approved
Zhang, Z., Rao, W., Di, X., Zhao, P., Xu, X. & Ben Abdesslem, F. (2018). Demo Abstract: Frequent Pattern-based Trajectory Completion. In: Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems: . Paper presented at SenSys '18 Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems (pp. 311-312).
Open this publication in new window or tab >>Demo Abstract: Frequent Pattern-based Trajectory Completion
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2018 (English)In: Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems, 2018, p. 311-312Conference paper, Published paper (Other academic)
Abstract [en]

GPS sensors have been widely used to track people's everyday life trajectories, generating massive trajectory datasets. The trajectory data typically contains sparse GPS points, and completing trajectories is often necessary. State-of-the-art methods [3, 4] essentially complete the entire route by using a single metric, e.g., either the shortest distance or the fastest driving/walking time. Unfortunately, using a single metric may not always work in real life due to the diversity of mobility patterns. In this demo abstract, we propose a frequent pattern (FP)-based trajectory completion approach, and demonstrate a system prototype to showcase the advantages of our approach over four previous works, in terms of accuracy and running time.

Series
SenSys ’18
National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-36465 (URN)10.1145/3274783.3275158 (DOI)2-s2.0-85061738410 (Scopus ID)
Conference
SenSys '18 Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems
Available from: 2018-12-17 Created: 2018-12-17 Last updated: 2019-03-29Bibliographically approved
Gong, Q., Chen, Y., Yu, X., Xu, C., Guo, Z., Xiao, Y., . . . Hui, P. (2018). 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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2018 (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: 2020-01-10Bibliographically approved
Lv, Q., Qiao, Y., Zhang, Y., Ben Abdesslem, F., Lin, W. & Yang, J. (2018). Measuring Geospatial Properties: Relating Online Content Browsing Behaviors to Users’ Points of Interest. Wireless personal communications, 101(3), 1469-1498
Open this publication in new window or tab >>Measuring Geospatial Properties: Relating Online Content Browsing Behaviors to Users’ Points of Interest
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2018 (English)In: Wireless personal communications, ISSN 0929-6212, E-ISSN 1572-834X, Vol. 101, no 3, p. 1469-1498Article in journal (Refereed) Published
Abstract [en]

With the growth of the Mobile Internet, people have become active in both the online and offline worlds. Investigating the relationships between users’ online and offline behaviors is critical for personalization and content caching, as well as improving urban planning. Although some studies have measured the spatial properties of online social relationships, there have been few in-depth investigations of the relationships between users’ online content browsing behaviors and their real-life locations. This paper provides the first insight into the geospatial properties of online content browsing behaviors from the perspectives of both geographical regions and individual users. We first analyze the online browsing patterns across geographical regions. Then, a multilayer-network-based model is presented to discover how inter-user distances affect the distributions of users with similar online browsing interests. Drawing upon results from a comprehensive study of users of three popular online content services in a metropolitan city in China, we achieve a broad understanding of the general and specific geospatial properties of users’ various preferences. Specifically, users with similar online browsing interests exhibit, to a large extent, strong geographic correlations, and different services exhibit distinct geospatial properties in terms of their usage patterns. The results of this work can potentially be exploited to improve a vast number of applications. 

Keywords
Geospatial properties, Multilayer network, Online content browsing, Online content services, Multilayers, Different services, Geo-spatial, Metropolitan cities, Multi-layer network, On-line contents, Points of interest, Social relationships, Spatial properties, Geographical regions
National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-33884 (URN)10.1007/s11277-018-5773-7 (DOI)2-s2.0-85046546444 (Scopus ID)
Available from: 2018-05-30 Created: 2018-05-30 Last updated: 2019-06-17Bibliographically approved
Ben Abdesslem, F., Abrahamsson, H. & Ahlgren, B. (2018). Measuring Mobile Network Multi-Access for Time-Critical C-ITS Applications. In: TMA 2018 - Proceedings of the 2nd Network Traffic Measurement and Analysis Conference: . Paper presented at 2nd Network Traffic Measurement and Analysis Conference, TMA 2018, 26 June 2018 through 29 June 2018.
Open this publication in new window or tab >>Measuring Mobile Network Multi-Access for Time-Critical C-ITS Applications
2018 (English)In: TMA 2018 - Proceedings of the 2nd Network Traffic Measurement and Analysis Conference, 2018Conference paper, Published paper (Refereed)
Abstract [en]

Cooperative Intelligent Transport Systems (C-ITS) make road traffic safer and more efficient, but require the mobile networks to handle time-critical applications. While some applications may need new dedicated communications technologies such as IEEE 802.11p or 5G, other applications can use current cellular networks. This study evaluates the performance that connected vehicles can expect from existing networks, and estimates the potential gain of multi-access by simultaneously transmitting over several operators. We upload time-critical warning messages from buses in Sweden, and characterise transaction times and network availability. We conduct the experiments with different protocols: UDP, TCP, and HTTPS. Our results show that when using UDP, the median transaction time for sending a typical warning message is 135 ms. We also show that multi-access can bring this value down to 73 ms. For time-critical applications requiring transaction times under 200 ms, multi-access can increase availability of the network from to 57.4% to 92.0%.

Keywords
Intelligent systems, Intelligent vehicle highway systems, Mobile telecommunication systems, Traffic control, Wireless networks, Cellular network, Communications technology, Connected vehicles, Different protocols, Intelligent transport systems, ITS applications, Network availability, Time-critical applications, 5G mobile communication systems
National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-36597 (URN)10.23919/TMA.2018.8506551 (DOI)2-s2.0-85057249133 (Scopus ID)9783903176096 (ISBN)
Conference
2nd Network Traffic Measurement and Analysis Conference, TMA 2018, 26 June 2018 through 29 June 2018
Available from: 2018-12-17 Created: 2018-12-17 Last updated: 2018-12-17Bibliographically approved
Ben Abdesslem, F., Abrahamsson, H. & Ahlgren, B. (2018). Measuring Mobile Network Multi-Access for Time-Critical C-ITS Applications. In: : . Paper presented at Network Traffic Measurement and Analysis Conference (TMA 2018). , Article ID 8506551.
Open this publication in new window or tab >>Measuring Mobile Network Multi-Access for Time-Critical C-ITS Applications
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Cooperative Intelligent Transport Systems (C-ITS)make road traffic safer and more efficient, but require the mobilenetworks to handle time-critical applications. While some applicationsmay need new dedicated communications technologiessuch as IEEE 802.11p or 5G, other applications can use currentcellular networks. This study evaluates the performance that connectedvehicles can expect from existing networks, and estimatesthe potential gain of multi-access by simultaneously transmittingover several operators. We upload time-critical warning messagesfrom buses in Sweden, and characterise transaction times andnetwork availability. We conduct the experiments with differentprotocols: UDP, TCP, and HTTPS. Our results show that whenusing UDP, the median transaction time for sending a typicalwarning message is 135 ms. We also show that multi-access canbring this value down to 73 ms. For time-critical applicationsrequiring transaction times under 200 ms, multi-access canincrease availability of the network from to 57.4% to 92.0%.

National Category
Communication Systems
Identifiers
urn:nbn:se:ri:diva-34314 (URN)10.23919/TMA.2018.8506551 (DOI)
Conference
Network Traffic Measurement and Analysis Conference (TMA 2018)
Available from: 2018-07-30 Created: 2018-07-30 Last updated: 2019-01-09Bibliographically approved
Abrahamsson, H., Ben Abdesslem, F., Ahlgren, B., Björkman, M., Brunstrom, A. & Marsh, I. (2018). Selecting Operator in 3G/4G Networks for Time-Critical C-ITS Applications. In: : . Paper presented at 14th Swedish National Computer Networking Workshop (SNCNW 2018).
Open this publication in new window or tab >>Selecting Operator in 3G/4G Networks for Time-Critical C-ITS Applications
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2018 (English)Conference paper, Published paper (Refereed)
National Category
Communication Systems
Identifiers
urn:nbn:se:ri:diva-34312 (URN)
Conference
14th Swedish National Computer Networking Workshop (SNCNW 2018)
Available from: 2018-07-30 Created: 2018-07-30 Last updated: 2019-01-22Bibliographically approved
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-7866-143x

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