Insight Centre for Data Analytics - Journal Articles

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An adaptive large neighbourhood search algorithm for diameter bounded network design problems
(Springer Nature Switzerland AG, 2021-06-23) Garraffa, Michele; Mehta, Deepak; O'Sullivan, Barry; Ozturk, Cemalettin; Quesada, Luis; Seventh Framework Programme; Science Foundation Ireland
This paper focuses on designing a diameter - constrained network where the maximum distance between any pair of nodes is bounded. The objective considered is to minimise a weighted sum of the total length of the links followed by the total length of the paths between the pairs of nodes. First, the problem is formulated in terms of Mixed Integer Linear Programming and Constraint Programming to provide two alternative exact approaches. Then, an adaptive large neighbourhood search (LNS) to overcome memory and runtime limitations of the exact methods in large size instances is proposed. Such approach is based on computing an initial solution and repeatedly improve it by solving relatively small subproblems. We investigate various alternatives for finding an initial solution and propose two different heuristics for selecting subproblems. We have introduced a tighter lower bound, which demonstrates the quality of the solution obtained by the proposed approach. The performance of the proposed approach is assessed using three real-world network topologies from Ireland, UK and Italy, which are taken from national telecommunication operators and are used to design a transparent optical core network. Our results demonstrate that the LNS approach is scalable to large networks and it can compute very high quality solutions that are close to being optimal.
Analyzing and improving stability of matrix factorization for recommender systems
(Springer, 2022-01-27) D'Amico, Edoardo; Gabbolini, Giovanni; Bernardis, Cesare; Cremonesi, Paolo; Science Foundation Ireland; European Regional Development Fund
Thanks to their flexibility and scalability, collaborative embedding-based models are widely employed for the top-N recommendation task. Their goal is to jointly represent users and items in a common low-dimensional embedding space where users are represented close to items for which they expressed a positive preference. The training procedure of these techniques is influenced by several sources of randomness, that can have a strong impact on the embeddings learned by the models. In this paper we analyze this impact on Matrix Factorization (MF). In particular, we focus on the effects of training the same model on the same data, but with different initial values for the latent representations of users and items. We perform several experiments employing three well known MF implementations over five datasets. We show that different random initializations lead the same MF technique to generate very different latent representations and recommendation lists. We refer to these inconsistencies as instability of representations and instability of recommendations, respectively. We report that stability of item representations is positively correlated to the accuracy of the model. We show that the stability issues affect also the items for which the recommender correctly predicts positive preferences. Moreover, we highlight that the effect is stronger for less popular items. To overcome these drawbacks, we present a generalization of MF called Nearest Neighbors Matrix Factorization (NNMF). The new framework learns the embedding of each user and item as a weighted linear combination of the representations of the respective nearest neighbors. This strategy has the effect to propagate the information about items and users also to their neighbors and allows the embeddings of users and items with few interactions to be supported by a higher amount of information. To empirically demonstrate the advantages of the new framework, we provide a detailed description of the NNMF variants of three common MF techniques. We show that NNMF models, compared to their MF counterparts, largely improve the stability of both representations and recommendations, obtain a higher and more stable accuracy performance, especially on long-tail items, and reach convergence in a fraction of epochs.
An application of belief merging for the diagnosis of oral cancer
(Elsevier, 2017-04-18) Kareem, Sameem Abdul; Pozos-Parra, Pilar; Wilson, Nic; Universiti Malaya; Consejo Nacional de Ciencia y Tecnología; Universidad Juárez Autónoma de Tabasco
Machine learning employs a variety of statistical, probabilistic, fuzzy and optimization techniques that allow computers to “learn” from examples and to detect hard-to-discern patterns from large, noisy or complex datasets. This capability is well-suited to medical applications, and machine learning techniques have been frequently used in cancer diagnosis and prognosis. In general, machine learning techniques usually work in two phases: training and testing. Some parameters, with regards to the underlying machine learning technique, must be tuned in the training phase in order to best “learn” from the dataset. On the other hand, belief merging operators integrate inconsistent information, which may come from different sources, into a unique consistent belief set (base). Implementations of merging operators do not require tuning any parameters apart from the number of sources and the number of topics to be merged. This research introduces a new manner to “learn” from past examples using a non parametrised technique: belief merging. The proposed method has been used for oral cancer diagnosis using a real-world medical dataset. The results allow us to affirm the possibility of training (merging) a dataset without having to tune the parameters. The best results give an accuracy of greater than 75%.
Assessment of hip and knee joints and implants using acoustic emission monitoring: A scoping review
(Institute of Electrical and Electronics Engineers, IEEE, 2020-12) Khokhlova, Liudmila; Komaris, Dimitrios-Sokratis; Tedesco, Salvatore; O'Flynn, Brendan; Science Foundation Ireland; European Regional Development Fund
Objectives: Population ageing and the subsequent increase of joint disorders prevalence requires the development of non-invasive and early diagnostic methods to enable timely medical assistance and promote healthy aging. Over the last decades, acoustic emission (AE) monitoring, a technique widely used in non-destructive testing, has also been introduced in orthopedics as a diagnostic tool. This review aims to synthesize the literature on the use of AE monitoring for the assessment of hip and knee joints or implants, highlighting the practical aspects and implementation considerations. Methods: this review was conducted as per the PRISMA statement for scoping reviews. All types of studies, with no limits on date of publication, were considered. Articles were assessed and study design parameters and technical characteristics were extracted from relevant studies. Results: conducted search identified 1379 articles and 64 were kept for charting. Seven additional articles were added at a later stage. Reviewed works were grouped into studies on joint condition assessment, implant assessment, and hardware or software development. Native knees and hip implants were most commonly assessed. The most researched conditions were osteoarthritis, implant loosening or squeaking in vivo and structural damage of implants in vitro. Conclusion: in recent years, AE monitoring showed potential of becoming a useful diagnostic tool for lower limb pathologies. However, further research is needed to refine the existing methods and assess their feasibility in early diagnostics. Significance: The current state of research on AE monitoring for hip and knee joint assessment is described and future research directions are identified.
Assessment of the prognostic value of radiomic features in 18F-FMISO PET imaging of hypoxia in postsurgery brain cancer patients: secondary analysis of imaging data from a single-center study and the multicenter ACRIN 6684 trial
(MDPI, 2020-03-01) Muzi, Mark; Wolsztynski, Eric; Fink, James R.; O'Sullivan, Janet N.; O'Sullivan, Finbarr; Krohn, Kenneth A.; Mankoff, David A.; National Institutes of Health; National Cancer Institute; Science Foundation Ireland
Hypoxia is associated with resistance to radiotherapy and chemotherapy in malignant gliomas, and it can be imaged by positron emission tomography with 18F-fluoromisonidazole (18F-FMISO). Previous results for patients with brain cancer imaged with 18F-FMISO at a single center before conventional chemoradiotherapy showed that tumor uptake via T/Bmax (tissue SUVmax/blood SUV) and hypoxic volume (HV) was associated with poor survival. However, in a multicenter clinical trial (ACRIN 6684), traditional uptake parameters were not found to be prognostically significant, but tumor SUVpeak did predict survival at 1 year. The present analysis considered both study cohorts to reconcile key differences and examine the potential utility of adding radiomic features as prognostic variables for outcome prediction on the combined cohort of 72 patients with brain cancer (30 University of Washington and 42 ACRIN 6684). We used both 18F-FMISO intensity metrics (T/Bmax, HV, SUV, SUVmax, SUVpeak) and assessed radiomic measures that determined first-order (histogram), second-order, and higher-order radiomic features of 18F-FMISO uptake distributions. A multivariate model was developed that included age, HV, and the intensity of 18F-FMISO uptake. HV and SUVpeak were both independent predictors of outcome for the combined data set (P < .001) and were also found significant in multivariate prognostic models (P < .002 and P < .001, respectively). Further model selection that included radiomic features showed the additional prognostic value for overall survival of specific higher order texture features, leading to an increase in relative risk prediction performance by a further 5%, when added to the multivariate clinical model.
Assigning and scheduling service visits in a mixed urban/rural setting
(World Scientific Publishing Company, 2020-06-18) Antunes, Mark; Armant, Vincent; Brown, Kenneth N.; Desmond, Daniel; Escamocher, Guillaume; George, Anne-Marie; Grimes, Diarmuid; O'Keeffe, Mike; Lin, Yiqing; O'Sullivan, Barry; Ozturk, Cemalettin; Quesada, Luis; Siala, Mohamed; Simonis, Helmut; Wilson, Nic; United Technologies Corporation, United States; Science Foundation Ireland; European Regional Development Fund
This paper describes a maintenance scheduling application, which was developed together with an industrial partner. This is a highly combinatorial decision process, to plan and schedule the work of a group of travelling repair technicians, which perform preventive and corrective maintenance tasks at customer locations. Customers are located both in urban areas, where many customers are in close proximity, and in sparsely populated rural areas, where the travel time between customer sites is significant. To balance the workload for the agents, we must consider both the productive working time, as well as the travel between locations. As the monolithic problem formulation is unmanageable, we introduce a problem decomposition into multiple sequential steps, that is compatible with current management practice. We present and compare different models for the solution steps, and discuss results on datasets provided by the industrial partner.
An axiomatic framework for influence diagram computation with partially ordered preferences
(Elsevier B.V., 2020-07-08) Wilson, Nic; Marinescu, Radu
This paper presents an axiomatic framework for influence diagram computation, which allows reasoning with partially ordered values of utility. We show how an algorithm based on sequential variable elimination can be used to compute the set of maximal values of expected utility (up to an equivalence relation). Formalisms subsumed by the framework include decision making under uncertainty based on multi-objective utility, or on interval-valued utilities, as well as a more qualitative decision theory based on order of magnitude probabilities and utilities. Consequently, we also introduce the order of magnitude influence diagram to model and solve partially specified sequential decision problems when only qualitative (or imprecise) information is available.
The benefit of receding horizon control: near-optimal policies for stochastic inventory control
(Elsevier Ltd., 2019-07-24) Dural-Selcuk, Gozdem; Rossi, Roberto; Kilic, Onur A.; Tarim, S. Armagan
In this paper we address the single-item, single-stocking point, non-stationary stochastic lot-sizing problem under backorder costs. It is well known that the (s, S) policy provides the optimal control for such inventory systems. However the computational difficulties and the nervousness inherent in (s, S) paved the way for the development of various near-optimal inventory control policies. We provide a systematic comparison of these policies and present their expected cost performances. We further show that when these policies are used in a receding horizon framework the cost performances improve considerably and differences among policies become insignificant.
Biosensing in dermal interstitial fluid using microneedle based electrochemical devices
(Elsevier, 2020-05-15) Madden, Julia; O'Mahony, Conor; Thompson, Michael; O'Riordan, Alan; Galvin, Paul; Science Foundation Ireland
This article explores recent advances in the development of electrochemical biosensors on microneedle platforms towards on-device sensing of biomarkers present in dermal interstitial fluid. The integration of a biosensor with a microneedle platform opens the possibility for minimally invasive bio-chemical detection or continuous monitoring within the dermal interstitial fluid. An introduction to interstitial fluid is provided placing emphasis on sampling methods that have been employed to extract and/or sample tissue fluid for analysis. We look briefly at microneedle technologies used to extract dermal interstitial fluid for subsequent analysis. Successive content will focus on microneedle technologies which have been integrated with electrochemical biosensors for the quantification of various metabolites, electrolytes and other miscellaneous entities known to be present in the dermal interstitial fluid. The review concludes with some of the key challenges and opportunities faced by this next-generation wearable sensing technology.
Blockchain-based digital twins collaboration for smart pandemic alerting: Decentralized COVID-19 pandemic alerting use case
(Hindawi, 2022-01-13) Sahal, Radhya; Alsamhi, Saeed H.; Brown, Kenneth N.; O'Shea, Donna; Science Foundation Ireland; European Regional Development Fund; H2020 Marie Skłodowska-Curie Actions; Taif University
Emerging technologies such as digital twins, blockchain, Internet of Things (IoT), and Artificial Intelligence (AI) play a vital role in driving the industrial revolution in all domains, including the healthcare sector. As a result of COVID-19 pandemic outbreak, there is a significant need for medical cyber-physical systems to adopt these emerging technologies to combat COVID-19 paramedic crisis. Also, acquiring secure real-time data exchange and analysis across multiple participants is essential to support the efforts against COVID-19. Therefore, we have introduced a blockchain-based collaborative digital twins framework for decentralized epidemic alerting to combat COVID-19 and any future pandemics. The framework has been proposed to bring together the existing advanced technologies (i.e., blockchain, digital twins, and AI) and then provide a solution to decentralize epidemic alerting to combat COVID-19 outbreaks. Also, we have described how the conceptual framework can be applied in the decentralized COVID-19 pandemic alerting use case.
Candidate selection and instance ordering for realtime algorithm configuration
(IOS Press, 2019-03-14) Fitzgerald, Tadhg; O'Sullivan, Barry; Science Foundation Ireland
Many modern combinatorial solvers have a variety of parameters through which a user can customise their behaviour. Algorithm configuration is the process of selecting good values for these parameters in order to improve performance. Time and again algorithm configuration has been shown to significantly improve the performance of many algorithms for solving challenging computational problems. Automated systems for tuning parameters regularly out-perform human experts, sometimes but orders of magnitude. Online algorithm configurators, such as ReACTR, are able to tune a solver online without incurring costly offline training. As such ReACTR’s main focus is on runtime minimisation while solving combinatorial problems. To do this ReACTR adopts a one-pass methodology where each instance in a stream of instances to be solved is considered only as it arrives. As such ReACTR’s performance is sensitive to the order in which instances arrive. It is still not understood which instance orderings positively or negatively effect the performance of ReACTR. This paper investigates the effect of instance ordering and grouping by empirically evaluating different instance orderings based on difficulty and feature values. Though the end use is generally unable to control the order in which instances arrive it is important to understand which orderings impact Re- ACTR’s performance and to what extent. This study also has practical benefit as such orderings can occur organically. For example as business grows the problems it may encounter, such as routing or scheduling, often grow in size and difficulty. ReACTR’s performance also depends strongly configuration selection procedure used. This component controls which configurations are selected to run in parallel from the internal configuration pool. This paper evaluates various ranking mechanisms and different ways of combining them to better understand how the candidate selection procedure affects realtime algorithm configuration. We show that certain selection procedures are superior to others and that the order which instances arrive in determines which selection procedure performs best. We find that both instance order and grouping can significantly affect the overall solving time of the online automatic algorithm configurator ReACTR. One of the more surprising discoveries is that having groupings of similar instances can actually negatively impact on the overall performance of the configurator. In particular we show that orderings based on nearly any instance feature values can lead to significant reductions in total runtime over random instance orderings. In addition, certain candidate selection procedures are more suited to certain orderings than others and selecting the correct one can show a marked improvement in solving times.
The change matters! Measuring the effect of changing the leader in joint music performances
(Institute of Electrical and Electronics Engineers (IEEE), 2019-11-04) Varni, G.; Mancini, Maurizio; Fadiga, L.; Camurri, A.; Volpe, G.
In a joint action, a group of individuals coordinate their movements to reach a shared goal. When a change - i.e., an event that affects group functioning - occurs, the group adopts strategies to face it. This paper investigates how a change involving a strategic core role in a group affects interpersonal coordination and ultimately group effectiveness in performing a joint action. Following the entrainment theory, interpersonal coordination is addressed in terms of the rhythmic cycles of the individuals and of the group and their adjustment. Music is used as an ideal ecological scenario for investigation. Results show that whereas the change of conductor had a limited significant effect on entrainment, a significant effect was found when entrainment is used as a predictor of the external ratings. Both the obtained results and the techniques developed for measuring entrainment may open novel research directions in the area of automated analysis of group behavior, and particularly of emotion in groups.
Citizenship attitudes and social inequality among Moroccan university students
(Taylor & Francis, 2022-07-11) Idrissi, Hajar; Takky, Salma; Idrissi, Hind; Science Foundation Ireland
Drawing on social identity approach, comprising of social identity theory and self-categorisation theory, this article compares the ways in which public and private university students in Morocco approach the controversial relationship between citizenship and identity. By revealing students’ self-identification and the role socio-economic factors have in this process, we seek to gain knowledge about the extent to which citizenship is perceived as a legal status as opposed to membership in a political community and how the transformation inherent in global market capitalism and the distribution of resources affect the youth’s behaviours and attitudes towards social action. The sample represented the public and private dichotomy divide through 150 participants from four differently located Moroccan universities, namely Sidi Mohamed Ben Abdellah University, Mohammed V University, Al-Akhawayn University and International University of Rabat. Data were collected by means of a self-administered questionnaire and a semi-structured interview and were analysed using a mixed method approach to triangulate findings and ensure trustworthiness.
Classifier-based constraint acquisition
(Springer, 2021-04-17) Prestwich, Steven D.; Freuder, Eugene C.; O'Sullivan, Barry; Browne, David; Science Foundation Ireland; European Regional Development Fund
Modeling a combinatorial problem is a hard and error-prone task requiring significant expertise. Constraint acquisition methods attempt to automate this process by learning constraints from examples of solutions and (usually) non-solutions. Active methods query an oracle while passive methods do not. We propose a known but not widely-used application of machine learning to constraint acquisition: training a classifier to discriminate between solutions and non-solutions, then deriving a constraint model from the trained classifier. We discuss a wide range of possible new acquisition methods with useful properties inherited from classifiers. We also show the potential of this approach using a Naive Bayes classifier, obtaining a new passive acquisition algorithm that is considerably faster than existing methods, scalable to large constraint sets, and robust under errors.
Cognitive functional therapy compared with a group-based exercise and education intervention for chronic low back pain: a multicentre randomised controlled trial (RCT)
(BMJ Publishing Group, 2019-10-19) O'Keeffe, Mary; O'Sullivan, Peter; Purtill, Helen; Bargery, Norma; O'Sullivan, Kieran; University of Limerick
Background One-size-fits-all interventions reduce chronic low back pain (CLBP) a small amount. An individualised intervention called cognitive functional therapy (CFT) was superior for CLBP compared with manual therapy and exercise in one randomised controlled trial (RCT). However, systematic reviews show group interventions are as effective as one-to-one interventions for musculoskeletal pain. This RCT investigated whether a physiotherapist-delivered individualised intervention (CFT) was more effective than physiotherapist-delivered group-based exercise and education for individuals with CLBP. Methods 206 adults with CLBP were randomised to either CFT (n=106) or group-based exercise and education (n=100). The length of the CFT intervention varied according to the clinical progression of participants (mean=5 treatments). The group intervention consisted of up to 6 classes (mean=4 classes) over 6–8 weeks. Primary outcomes were disability and pain intensity in the past week at 6 months and 12months postrandomisation. Analysis was by intention-to-treat using linear mixed models. Results CFT reduced disability more than the group intervention at 6 months (mean difference, 8.65; 95% CI 3.66 to 13.64; p=0.001), and at 12 months (mean difference, 7.02; 95% CI 2.24 to 11.80; p=0.004). There were no between-group differences observed in pain intensity at 6 months (mean difference, 0.76; 95% CI -0.02 to 1.54; p=0.056) or 12 months (mean difference, 0.65; 95% CI -0.20 to 1.50; p=0.134). Conclusion CFT reduced disability, but not pain, at 6 and 12 months compared with the group-based exercise and education intervention. Future research should examine whether the greater reduction in disability achieved by CFT renders worthwhile differences for health systems and patients. Trial registration number ClinicalTrials.gov registry (NCT02145728).
Cognitive radio for disaster response networks: survey, potential, and challenges
(IEEE, 2014-10-31) Ghafoor, Saim; Sutton, Paul D.; Sreenan, Cormac J.; Brown, Kenneth N.; Science Foundation Ireland
In the wake of a natural or man-made disaster, restoration of telecommunications is essential. First responders must coordinate their responses, immediate casualties require assistance, and all affected citizens may need to access information and contact friends and relatives. Existing access and core infrastructure may be damaged or destroyed, so to support the required services, new infrastructure must be rapidly deployed and integrated with undamaged resources still in place. This new equipment should be flexible enough to interoperate with legacy systems and heterogeneous technologies. The ability to selforganize is essential in order to minimize any delays associated with manual configuration. Finally, it must be robust and reliable enough to support mission-critical applications. Wireless systems can be more easily reconfigured than wired solutions to adapt to the various changes in the operating environment that can occur in a disaster scenario. A cognitive radio is one that can observe its operating environment, make decisions and reconfigure in response to these observations, and learn from experience. This article examines the use of cognitive radio technologies for disaster response networks and shows that they are ideally suited to fulfill the unique requirements of these networks. Key enabling technologies for realizing real-world cognitive radio networks for disaster response are discussed and core challenges are examined.
A cognitive radio-based fully blind multihop rendezvous protocol for unknown environments
(Elsevier, 2020-07-19) Ghafoor, Saim; Sreenan, Cormac J.; Brown, Kenneth N.
In Cognitive Radio networking, the blind rendezvous problem is when two or more nodes must establish a link, but where they have no predefined schedule or common control channel for doing so. The problem becomes more challenging when the information about the existence of other nodes in the network, their topology, and primary user activity are also unknown, identified here as a fully blind rendezvous problem. In this paper, a novel and fully blind multihop (FBM) rendezvous framework is proposed with an extended modular clock algorithm (EMCA). The EMCA-FBM is a fully blind multihop rendezvous protocol as it assumes the number of nodes, primary radio activity and topology information as unknown. It is shown to work with different Cognitive Radio operating policies to achieve adaptiveness towards the unknown primary radio activity, and self-organization for autonomously handling the rendezvous process by using transmission schedules. It is capable of working without any information of neighbor nodes and terminating the rendezvous process whenever all or sufficient nodes are discovered. The proposed FBM is also shown to work as a general framework to extend existing single hop rendezvous protocols to work as a multihop rendezvous protocol. In comparison with other modified blind rendezvous strategies for multihop network, the combination of the proposed EMCA-FBM protocol and operating policies is shown to be effective in improving the average time to rendezvous (up to 70%) and neighbor discovery accuracy (almost 100%) while reducing harmful interference.
Combinatorial search from an energy perspective
(Elsevier, 2019-05-03) Siala, Mohamed; O'Sullivan, Barry; Science Foundation Ireland; European Regional Development Fund
Most studies related to parallel and portfolio search for solving combinatorial problems, such as those found in Boolean satisfiability or constraint programming, evaluate search cost in terms of runtime. However, given the complex computing architectures available today and the focus on the environmental impact of computing, there is growing interest in also considering the energy cost associated with solving these problems. In the context of combinatorial problem-solving, a simple approximation of energy cost is the product of the number of machines multiplied by the runtime spent to solve a problem instance. However, the picture is much more complex due to the impact that the distribution of runtimes, even for solving a single specific instance, can have on search cost. In this paper we present an initial, but comprehensive, study on the impact of runtime distribution on the amount of energy required for combinatorial problem solving characterised by two common continuous runtime distributions, namely the Weibull and Pareto distributions. The primary contribution of this paper is to demonstrate that there is an interesting and non-trivial relationship between runtime, parallelisation, and energy cost in combinatorial solving that is worthy of further study.
Comparing person-specific and independent models on subject-dependent and independent human activity recognition performance
(MDPI, 2020-06-29) Scheurer, Sebastian; Tedesco, Salvatore; O'Flynn, Brendan; Brown, Kenneth N.; Science Foundation Ireland; European Regional Development Fund; Seventh Framework Programme; Enterprise Ireland
The distinction between subject-dependent and subject-independent performance is ubiquitous in the human activity recognition (HAR) literature. We assess whether HAR models really do achieve better subject-dependent performance than subject-independent performance, whether a model trained with data from many users achieves better subject-independent performance than one trained with data from a single person, and whether one trained with data from a single specific target user performs better for that user than one trained with data from many. To those ends, we compare four popular machine learning algorithms’ subject-dependent and subject-independent performances across eight datasets using three different personalisation–generalisation approaches, which we term person-independent models (PIMs), person-specific models (PSMs), and ensembles of PSMs (EPSMs). We further consider three different ways to construct such an ensemble: unweighted, κ -weighted, and baseline-feature-weighted. Our analysis shows that PSMs outperform PIMs by 43.5% in terms of their subject-dependent performances, whereas PIMs outperform PSMs by 55.9% and κ -weighted EPSMs—the best-performing EPSM type—by 16.4% in terms of the subject-independent performance.
Computational Commensality: from theories to computational models for social food preparation and consumption in HCI
(Frontiers Media, 2019-12-05) Niewiadomski, Radoslaw; Ceccaldi, Eleonora; Huisman, Gijs; Volpe, Gualtiero; Mancini, Maurizio
Food and eating are inherently social activities taking place, for example, around the dining table at home, in restaurants, or in public spaces. Enjoying eating with others, often referred to as “commensality,” positively affects mealtime in terms of, among other factors, food intake, food choice, and food satisfaction. In this paper we discuss the concept of “Computational Commensality,” that is, technology which computationally addresses various social aspects of food and eating. In the past few years, Human-Computer Interaction started to address how interactive technologies can improve mealtimes. However, the main focus has been made so far on improving the individual's experience, rather than considering the inherently social nature of food consumption. In this survey, we first present research from the field of social psychology on the social relevance of Food- and Eating-related Activities (F&EA). Then, we review existing computational models and technologies that can contribute, in the near future, to achieving Computational Commensality. We also discuss the related research challenges and indicate future applications of such new technology that can potentially improve F&EA from the commensality perspective.

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