Search

International audience; Disasters are becoming more and more common around the world, making technology important to guarantee people’s lives as much as possible.One of the most modern advances of recent years is how AI is used in disaster relief. Researchers propose works based on new technologies (IoT, Cloud Computing, Blockchain, etc.) and AI concepts (Machine Learning, Natural Language Processing, etc.). But these concepts are difficult to exploit in low and middle socio-demographic index (SDI) countries, especially as most disasters happen in.In this paper we propose S2S intelligent system, based on voice recognition to life-saving in disaster relief. Generally, a disaster victim is enable to access to his Smartphone and ask help, with this system, saying “help” will be enough to send automatically alerts to the nearest Emergency Operation Services (EOS).S2S is composed of two parts: Intelligent application embedded on citizens and victims Smartphones, and S2S System for the Emergency Operation Services.

Part 4: Testing Methods and Automation; International audience; In recent years, there has been an increased application of machine learning (ML) to decision making systems. This has prompted an urgent need for validating requirements on ML models. Fairness is one such requirement to be ensured in numerous application domains. It specifies a software as “learned” by an ML algorithm to not be biased in the sense of discriminating against some attributes (like gender or age), giving different decisions upon flipping the values of these attributes.In this work, we apply verification-based testing (VBT) to the fairness checking of ML models. Verification-based testing employs verification technology to generate test cases potentially violating the property under interest. For fairness testing, we additionally provide a specification language for the formalization of different fairness requirements. From the ML model under test and fairness specification VBT automatically generates test inputs specific to the specified fairness requirement. The empirical evaluation on several benchmark ML models shows verification-based testing to perform better than existing fairness testing techniques with respect to effectiveness.

Part 3: eBusiness; International audience; Encouraged by the crucial need to understand merchant adoption of mobile payment, this study explores the role trust play in the adoption of mobile payment by merchant and the enablers for merchant’s trust in mobile payment systems. This was done by Conceptualising the characteristics of the service provider and technology characteristics as the two dimensions that could influence merchant adoption of mobile payments. The study was done through the lenses of the Technology Acceptance Model (TAM) and the Trust-Theoretic Model and adopted a qualitative approach where two merchants were selected from the health sector. The findings demonstrate that the role of merchant trust is very critical for adoption due to m-payment technology and security risk. Hence, sufficient trust-building structures in mobile payment space are essential for the adoption of mobile payment by merchants. Moreover, the findings indicate mobile service provider characteristics and the mobile technology characteristics are both imperative toward building trust in mobile payment systems for merchants’ adoption. The study also found that the trust of both technology and service provider has a far more critical influence on merchants’ adoption of mobile payments than perceived usefulness or ease of use. The study, therefore, recommends that service providers should consider the opportunity to nurture merchant trust because merchant trust acts as a fundamental enabler for the adoption of mobile payments. Other implications are also discussed.

Part 4: Testing Methods and Automation; International audience; Within the context of software evolution, due to time-to-market pressure, it is not uncommon that a company has not enough time and/or resources to re-execute the whole test suite on the new software version, to check for non-regression. To face this issue, many Regression Test Prioritization techniques have been proposed, aimed at ranking test cases in a way that tests more likely to expose faults have higher priority. Some of these techniques exploit code churn metrics, i.e. some quantification of code changes between two subsequent versions of a software artifact, which have been proven to be effective indicators of defect-prone components. In this paper, we first present three new Regression Test Prioritization strategies, based on a novel code churn metric, that we empirically assessed on an open source software system. Results highlighted that the proposal is promising, but that it might be further improved by a more detailed analysis on the nature of the changes introduced between two subsequent code versions. To this aim, in this paper we also sketch a more refined approach we are currently investigating, that quantifies changes in a code base at a finer grained level. Intuitively, we seek to prioritize tests that stress more fault-prone changes (e.g., structural changes in the control flow), w.r.t. those that are less likely to introduce errors (e.g., the renaming of a variable). To do so, we propose the exploitation of the Abstract Syntax Tree (AST) representation of source code, and to quantify differences between ASTs by means of specifically designed Tree Kernel functions, a type of similarity measure for tree-based data structures, which have shown to be very effective in other domains, thanks to their customizability.

Part 5: Big Data and Machine Learning; International audience; Big data has gained academic relevance over the last decade and is also of interest to other role-players such as governments, businesses and the general public. Based on our previous work on the Big Data Readiness Index (BDRI) we place the focus on one under-investigated aspect of big data: the linkage to happiness. The BDRI, applied on Africa, includes the topic of happiness within the digital wellbeing driver, but the link between the two topics requires further investigation. Thus, two underlying questions emerge: what is the relation between happiness and big data? And how does Africa perform in digital wellbeing? This paper includes a structured literature review highlighting five key clusters indicating this link. Furthermore, we present some first empirical insights using the BDRI focusing on Africa. Overall, the African continent performs best in the social inclusion cluster of happiness, with the most room for improvement in the job creation cluster.

Part 6: ICT and Education; International audience; Faced with many challenges resulting in learners’ poor performance at the matriculation level, emphasis on Science, Technology, Engineering and Mathematics (STEM) education is in its infancy in South African’s high schools. However, studies have shown that mobile learning (m-learning) can be used to mitigate the challenges of STEM education. Despite, the benefits of mobile learning to rural STEM learners, its full potential has not been realized because the adoption of m-learning depends on users’ acceptance. Prior studies focused on teachers’ and learners’ acceptance of mobile learning. However, little is known about parents’ acceptance of m-learning, especially in rural areas. This study explores the acceptance of m-learning by parents of rural high school STEM learners. The study proposes the parents’ acceptance of m-learning model, which extends the technology acceptance model by introducing perceived social influence and perceived resources. Stratified random sampling was used to select 200 parents in the survey. Partial least squares structural equation modeling (PSL-SEM) was used to analyze data from 129 valid questionnaires. The proposed model explained 41% of the variance in parents’ acceptance of mobile learning. Attitude towards the use was found to be the best predictor and the only factor that have a direct effect on behavioral intention to use mobile learning. However, all other factors have an indirect influence on behavioral intention. The findings revealed that for mobile learning to be successfully implemented in rural areas, resources need to be provided.

International audience; Traffic classification is key for managing both QoS and security in the Internet of Things (IoT). However, new traffic obfuscation techniques have been developed to thwart classification. Traffic mutation is one such obfuscation technique, that consists of modifying the flow’s statistical characteristics to mislead the traffic classifier. In fact, this same technique can also be used to hide normal traffic characteristics for the sake of privacy. However, the concern is its use by attackers to bypass intrusion detection systems by modifying the attack traffic characteristics. In this paper, we propose an unsupervised Deep Learning (DL)-based model to detect mutated traffic. This model is based on generative DL architectures, namely Autoencoders (AE) and Generative Adversarial Network (GAN). This model consists of a denoising AE to de-anonymize the mutated traffic and a discriminator to detect it. The implementation results show that the traffic can be denoised when different mutation techniques are applied with a reconstruction error less than $$10^{-1}$$. In addition, the detection rate of fake traffic reaches 83.7%.

International audience; Wide area networks are built to have enough resilience and flexibility, such as offering many paths between multiple pairs of end-hosts. To prevent congestion, current practices involve numerous tweaking of routing tables to optimize path computation, such as flow diversion to alternate paths or load balancing. However, this process is slow, costly and require difficult online decision-making to learn appropriate settings, such as flow arrival rate, workload, and current network environment. Inspired by recent advances in AI to manage resources, we present DeepRoute, a model-less reinforcement learning approach that translates the path computation problem to a learning problem. Learning from the network environment, DeepRoute learns strategies to manage arriving elephant and mice flows to improve the average path utilization in the network. Comparing to other strategies such as prioritizing certain flows and random decisions, DeepRoute is shown to improve average network path utilization to 30% and potentially reduce possible congestion across the whole network. This paper presents results in simulation and also how DeepRoute can be demonstrated by a Mininet implementation.

International audience; In this paper, we present a collaborative multi-agent based system for data mining. We have used two data mining model functions, clustering of variables in order to build homogeneous groups of attributes, association rules inside each of these groups and a multi-agent approach to integrate the both data mining techniques. For the association rules extraction, we use both apriori algorithm and genetic algorithm.The main goal of this paper is the evaluation of the association rules obtained by running apriori and genetic algorithm using quantitative datasets in multi agent environment.

International audience; This paper proposes a general-purpose anomaly detection mechanism for Internet backbone traffic named GAMPAL (General-purpose Anomaly detection Mechanism using Path Aggregate without Labeled data). GAMPAL does not require labeled data to achieve a general-purpose anomaly detection. For scalability to the number of entries in the BGP RIB (Routing Information Base), GAMPAL introduces path aggregates. The BGP RIB entries are classified into the path aggregates, each of which is identified with the first three AS numbers in the AS_PATH attribute. GAMPAL establishes a prediction model of traffic throughput based on past traffic throughput. It adopts the LSTM-RNN (Long Short-Term Memory Recurrent Neural Network) model focusing on periodicity in weekly scale of the Internet traffic pattern. The validity of GAMPAL is evaluated using the real traffic information and the BGP RIB exported from the WIDE backbone network (AS2500), a nation-wide backbone network for research and educational organizations in Japan. As a result, GAMPAL successfully detects traffic increases due to events and DDoS attacks targeted to a stub organization.

International audience; In recent years, the fast developments in hardware, software, networking and communication technologies have facilitated the big emergence of many technologies such as Internet of things. Measurement and collecting data from physical world, and then sending it to digital world is base of this technology. The transmitted data are stocked, processed and then possibly used to act upon the physical world. IoT adds intelligence and autonomy to many domains (e.g. health care, smart transportation and industrial monitoring). As a result, it makes human life more comfortable and simple. However, as all emerging technologies, IoT is suffering from several security challenges and issues, especially that most of IoT devices and sensors are resources- constrained devices. As security issues and attacks could put systems in dangerous and could threat human life too, this paper treats these problems. We will provide an overview about IoT technology, and we will present various security issues that target the perception and the network levels. Moreover, we will discuss how each layer is damaged by harmful and malicious purposes. Most of recent papers use the three layers architecture (which is an old architecture) to present security problems; but this paper uses one of the new reference architectures to study security threats and attacks.

International audience; Intrusion and anomaly detection are particularly important in the time of increased vulnerability in computer networks and communication. Therefore, this research aims to detect network intrusion with the highest accuracy and fastest time. To achieve this, nine supervised machine learning algorithms were first applied to the UNSW-NB15 dataset for network anomaly detection. In addition, different attacks are investigated with different mitigation techniques that help determine the types of attacks. Once detection was done, the feature set was reduced according to existing research work to increase the speed of the model without compromising accuracy. Furthermore, seven supervised machine learning algorithms were also applied to the newly released BoT-IoT dataset with around three million network flows. The results show that the Random Forest is the best in terms of accuracy (97.9121%) and Naïve Bayes the fastest algorithm with 0.69 s for the UNSW-NB15 dataset. C4.5 is the most accurate one (87.66%), with all the features considered to identify the types of anomalies. For BoT-IoT, six of the seven algorithms have a close to 100% detection rate, except Naïve Bayes.

International audience; Data clustering is one of the most important unsupervised classification method. It aims at organizing objects into groups (or clusters), in such a way that members in the same cluster are similar in some way and members belonging to different cluster are distinctive. Among other general clustering method, k-means is arguably the most popular one. However, it still has some inherent weaknesses. One of the biggest challenges when using k-means is to determine the optimal number of clusters, k. Although many approaches have been suggested in the literature, this is still considered as an unsolved problem. In this study, we propose a new technique to improve the gap statistic approach for selecting k. It has been tested on different datasets, on which it yields superior results compared to the original gap statistic. We expect our new method to also work well on other clustering algorithms where the number k is required. This is because our new approach, like the gap statistic, can work with any clustering method.

International audience; Traditionally, research in Network Security has largely focused on Intrusion Detection and the use of Machine Learning techniques towards identifying malicious agents as well as work on methods towards protecting ourselves from such attacks. In this paper, we wish to make use of the same techniques to analyze the profile of the attacker in the case of a DDoS attack on a distributed honeypot.

International audience; The design and development of future communications networks call for a careful examination of biological and social systems. New technological developments like self-driving cars, wireless sensor networks, drones swarm, Internet of Things, Big Data, and Blockchain are promoting an integration process that will bring together all those technologies in a large-scale heterogeneous network. Most of the challenges related to these new developments cannot be faced using traditional approaches, and require to explore novel paradigms for building computational mechanisms that allow us to deal with the emergent complexity of these new applications. In this article, we show that it is possible to use biologically and socially inspired computing for designing and implementing self-organizing communication systems. We argue that an abstract analysis of biological and social phenomena can be made to develop computational models that provide a suitable conceptual framework for building new networking technologies: biologically inspired computing for achieving efficient and scalable networking under uncertain environments; socially inspired computing for increasing the capacity of a system for solving problems through collective actions. We aim to enhance the state-of-the-art of these approaches and encourage other researchers to use these models in their future work.

International audience; To be able to identify computer attacks, detection systems that are based on faults are not dependent on data base upgrades unlike the ones based on misuse. The first type of systems mentioned generate a knowledge pattern from which the usual and unusual traffic is distinguished. Within computer networks, different classification traffic techniques have been implemented in intruder detection systems based on abnormalities. These try to improve the measurement that assess the performance quality of classifiers and reduce computational cost. In this research work, a comparative analysis of the obtained results is carried out after implementing different selection techniques such as Info.Gain, Gain ratio and Relief as well as Bayesian (Naïve Bayes and Bayesians Networks). Hence, 97.6% of right answers were got with 13 features. Likewise, through the implementation of both load balanced methods and attributes normalization and choice, it was also possible to diminish the number of features used in the ID classification process. Also, a reduced computational expense was achieved.

International audience; In this article we start from the observation that REST services are the most used as tools of interoperability and orchestration in the Internet of Things (IoT). But REST does not make it possible to inject artificial intelligence into connected objects, i.e. it cannot allow autonomy and decision-making by the objects themselves. To define an intelligence to a connected object, one can use a Believe Desire Intention agent (BDI an intelligent agent that adopts human behavior) such as Jason Agentspeak. But Jason AgentSpeak does not guarantee orchestration or choreography between connected objects. There are platforms for service orchestration and choreography in IoT, still the interconnection with artificial intelligence needs to be built. In this article, we propose a new approach called Jason-RS. It is a result of pairing Jason BDI agent with the web service technologies to exploit the agent capacity as a service, Jason-RS turn in Java SE and it does not need any middleware. The architecture that we propose allows to create the link between Artificial Intelligence and Services choreography to reduce human intervention in the service choreography. In order to validate the proposed approach, we have interconnected the Iot BeC3 platform and the REST agent (Jason-RS). The decision-making faculty offered by Jason-RS is derived from the information sent by the objects according to the different methods of REST (GET, POST, PUT, and DELETE) that Jason-RS offers. As a result, the objects feed the inter-agent collaborations and decision-making inside the agent. Finally, we show that Jason-RS allows the Web of Objects to power complex systems such as an artificial intelligence responsible for processing data. This performance is promising.

Part 4: Short Papers: Security, Intrusion Detection, and Configuration; International audience; With a vastly different header format, IPv6 introduces new vulnerabilities not possible in IPv4, potentially requiring new detection algorithms. While many attacks specific to IPv6 have proven to be possible and are described in the literature, no detection solutions for these attacks have been proposed. In this study we identify and characterise IPv6-specific attacks that can be detected using flow monitoring. By constructing flow-based signatures, detection can be performed using available technologies such as NetFlow and IPFIX. To validate our approach, we implemented these signatures in a prototype, monitoring two production networks and injecting attacks into the production traffic.

Part 2: Test Case Generation; International audience; Homing, synchronizing and distinguishing sequences (HSs, SSs, and DSs) are used in FSM (Finite State Machine) based testing for state identification and can significantly reduce the size of a returned test suite with guaranteed fault coverage. However, such preset sequences not always exist for nondeterministic FSMs and are rather long when existing. Adaptive HSs, SSs and DSs are known to exist more often and be much shorter that makes them attractive for deriving test suites and adaptive checking sequences. As nowadays, a number of specifications are represented by nondeterministic FSMs, the deeper study of such sequences, their derivation strategies, and related complexity estimations/reductions is in great demand. In this paper, we evaluate the complexity of deriving adaptive HSs and SSs for noninitialized FSMs, the complexity of deriving DSs for noninitialized merging-free FSMs.

International audience; The problem of generating tests detecting all logical and timing faults which can occur in real-time systems is challenging; this is because the number of (timing) faults is potentially too big or infinite. As a result, it might be time consuming to generate an important number of adequate tests. The traditional model based testing approach considers a fault domain as the universe of all machines with a given number of states and input-output alphabet while mutation based approaches define a list of mutants to kill with a test suite. In this paper, we combine the two approaches by developing a mutation testing technique for real-time systems represented with deterministic timed finite state machines with timed guards and timeouts (TFSM-TG). In this approach, fault domains consisting of fault-seeded versions of the specification (mutants) are represented with non-deterministic TFSM-TG. The test generation avoids the one-by-one enumeration of the mutants and is based on constraint solving. We present the results of an empirical proof-of-concept implementation of the proposed approach.

Part 6: Industrial Applications; International audience; This paper presents the main results of the experiments conducted using the MMT-IoT security analysis solution run on a IoT Fed4Fire+ platform (Virtual Wall - w.iLab proposed by IMEC, Belgium). MMT is a monitoring framework developed by Montimage, and MMT-IoT is the tool that allows monitoring and analysing the security and performance of IoT networks. The results obtained concern two principal advancements. First, the adaptations made to deploy MMT-IoT on the IoT platform in order to run the tool on the platform’s IoT devices. Second, the deployment of the software allowed us to run preliminary tests on the selected platform for performing initial validation and scalability tests on this real environment. To this end, Montimage defined and implemented three test scenarios related to security and scalability with 1 or more clients. These results will be used to prepare a new experimentation phase involving also another Fed4Fire+ platform (LOG-a-TEC proposed by IJS, Slovenia).

Part 1: Test and Artificial Intelligence; International audience; Regular expressions are widely used to describe and document regular languages, and to identify a set of (valid) strings. Often they are not available or known, and they must be learned or inferred. Classical approaches like L* make strong assumptions that normally do not hold. More feasible are testing approaches in which it is possible only to generate strings and check them with the underlying system acting as oracle. In this paper, we devise a method that starting from an initial guess of the regular expression, it repeatedly generates and feeds strings to the system to check whether they are accepted or not, and it tries to repair consistently the alleged solution. Our approach is based on an evolutionary algorithm in which both the population of possible solutions and the set of strings co-evolve. Mutation is used for the population evolution in order to produce the offspring. We run a set of experiments showing that the string generation policy is effective and that the evolutionary approach outperforms existing techniques for regular expression repair.

Part 5: Security and Performance Testing; International audience; Web services allow the communication of heterogeneous systems and are particularly suitable for building cloud applications. Furthermore, such applications must verify some static properties, but also tolerate attacks at runtime to ensure service continuity. To achieve this, in this paper we propose an attack tolerance framework that includes the risks of attacks. After describing the foundation of this framework, we propose expressing cloud applications as choreographies of services that take into account their distributed nature. Then, we extended the framework to introduce choreography verification by incorporating monitoring (passive tests) and reaction mechanisms. These techniques are validated through relevant experiments. As a result, our framework ensures the required attack tolerance of such cloud applications.

Part 1: E-Business; International audience; In this paper, we identify two distinct behavioral strategies for dealing with problems encountered in the use of mobile apps - fight or flight. In the fight strategy, individuals do not give up using an app when faced with a problem; rather, they experiment with different ways to cope with that problem, whereas the flight strategy refers to the user’s decision to uninstall an app when they encounter a problem and/or their intention to use an alternative app. These strategies were identified from an analysis of documents, which forty-two users reported, and can be used to understand how users deal with encountered problems. The participants were asked to use a mobile app of their choice for one week and report the behavioral strategies they utilized to counter problems they experienced. According to the findings obtained from content analysis, the most reported complaints concerned the categories of interface design, functional error, feature request, and feature removal. The participants who complained about functional errors, frustrating features, and slow application speed stopped using the app (flight behavior) whereas those that were dissatisfied with the interface, a missing feature or the content of the app continued to use the app and tried to overcome the problems (fight behavior).

Part 4: Artificial Intelligence and Internet of Things; International audience; Internet of Things (IoT) encompasses a wide range of devices and technologies which cumulatively shape a new environment with unprecedented business prospects. This paper aims to investigate the business potential of IoT, focusing on mobile IoT and IoT as a service. In this direction, it presents the case of RAWFIE, a H2020 project on a research and experimentation federated platform of mobile IoT testbeds and devices. The paper describes RAWFIE potential business models, analyzing them into their characteristics, based on the business model canvas, examining the possibilities as well as the challenges these entail, using a SWOT analysis, and testing them in a preliminary evaluation. The study offers research and practical insights as a starting point for business models of IoT as a service, focusing in the context of mobile IoT experimentation.

Part 1: E-Business; International audience; Despite the rapid growth in mobile stores (e.g., Apple Store, Google Play), scholarly research in this area is still in the early stages. In particular, there is a need for more empirical analysis of how the main features of these new systems shape the customer experience. This study aims to empirically identify and validate the key factors shaping users’ satisfaction toward mobile stores. The conceptual model was proposed based on a group of the main factors from the extended Unified Theory of Acceptance and Use of Technology (UTAUT2), mobile interactivity, and perceived security and privacy. The empirical analysis was conducted in Jordan by collecting data from a convenience sample of users of mobile stores. Structural equation modelling was applied to test the current study’s model. The results support the significant impact of performance expectancy, price value, hedonic motivation, personalization, responsiveness, and perceived security and privacy on user satisfaction. Discussion of the main limitations and future research directions are also provided.

Part 2: Big Data Analytics; International audience; In an information society, information has become one of the most valuable asserts to an organisation. This is even more important in the mining industry in Africa where production lines are highly sensitive and decision makers are dependable on correct information to make decisions. One of the systems that can provide for the information needs of an organisation - Business Intelligence (BI) systems - unfortunately has a high failure rate. Some of the reasons can be attributed to technical issues (such as data structures, data warehouses), process issues (information retrieval processes and analysis), human issues (resistance to adoption) and the complex nature of BI.This qualitative study investigated the adoption of BI systems by end users by considering the work environment and user empowerment as suggested by Kim and Gupta [1]. Data was gathered using semi-structured interviews considering both aspects of the work environment and user empowerment.The findings of the study suggested that a strong bureaucratic culture and strict safety regulatory requirements inhibits job autonomy. Job autonomy in return has a negative impact on the willingness of end users to create their own BI reports. Poor management support and a lack of training in the utilisation of BI systems furthermore make it difficult for the ageing workforce to use all the advanced features of the BI systems and capabilities. Finally, end users felt a lack of empowerment to make business decisions and therefor lack motivation to use the system.

Part 5: Smart Cities and Smart Homes; International audience; Recent technological advances have contributed to the development of smart homes, embedded with artificial intelligence, which aim to provide tailored services to residents. Smart home technologies benefit people daily and improve the environment in the long-term perspective. Despite the great interest of the research community in smart homes, the adoption rate is still low. The purpose of this study is to develop the research model, which can explain the acceptance of smart homes by users. Along with the relationship of technology acceptance factors with use behaviour, this study analyses the importance of individuals’ belief and values. Structural equational modelling has been employed to test the proposed hypotheses using a sample of 422 smart home users. The analysis revealed the significance of the relationships between values and perceived technology-fit, while the technology acceptance factors had a strong correlation with use behaviour leading to satisfaction.

Part 1: E-Business; International audience; Mobile payment (m-payment) apps have been introduced as an innovative alternative payment method that extends in-store payment options available to consumers. Despite being marketed as convenient and secure, recent research reports that m-payment uptake has gone far below earlier forecasts. This is due to consumers perceiving little added value relative to existing payment options, such as contactless cards. Augmenting m-payment with value added services (VAS) has been suggested as a way to add value to m-payment and boost demand. However, empirical investigation about the role and effect of VAS on consumers’ perceptions of m-payment value remains scant. This study attempts to fill this gap by employing a deductive qualitative approach through the lens of perceived value theory, extended with perceived trust and risk. A total of 23 interviews were conducted with UK adopters and nonadopters of m-payment. The findings suggest that the perceived added value of the augmented m-payment service was mainly derived from utilitarian values associated with the additional functionalities offered by VAS. Additionally, the augmentation of m-payment has enhanced perceptions of trust in the service provider as a result of integrating additional features that tackle issues associated with the payment experience. This study advances knowledge of the concept of added value in the m-payment context and provides practical suggestions to m-payment providers for increasing the consumer perceived value.

Part 4: Artificial Intelligence and Internet of Things; International audience; The value of Artificial Intelligence (AI) in augmenting or even replacing human decision-making in the organizational context is gaining momentum in the last few years. A growing number of organizations are now experimenting with different approaches to support and shape their operations. Nevertheless, there has been a disproportionate amount of attention on the potential and value that AI can deliver to private companies, with very limited empirical attention focusing on the private sector. The purpose of this research is to examine the current state of AI use in municipalities in Norway, what future aspirations are, as well as identify the challenges that exist in realizing them. To investigate these issues, we build on a survey study with respondents holding IT management positions in Norwegian municipalities. The results pinpoint to specific areas of AI applications that public bodies intend to invest in, as well as the most important challenges they face in making this transition.

Part 2: Big Data Analytics; International audience; Current applications developed for the Internet of Things (IoT) usually involve the processing of collected data for delivering analytics and support efficient decision making. The basis for any processing mechanism is data analysis, usually having as an outcome responses in various analytics queries defined by end users or applications. However, as already noted in the respective literature, data analysis cannot be efficient when missing values are present. The research community has already proposed various missing data imputation methods paying more attention of the statistical aspect of the problem. In this paper, we study the problem and propose a method that combines machine learning and a consensus scheme. We focus on the clustering of the IoT devices assuming they observe the same phenomenon and report the collected data to the edge infrastructure. Through a sliding window approach, we try to detect IoT nodes that report similar contextual values to edge nodes and base on them to deliver the replacement value for missing data. We provide the description of our model together with results retrieved by an extensive set of simulations on top of real data. Our aim is to reveal the potentials of the proposed scheme and place it in the respective literature.

Part 11: Modelling and Managing the Digital Enterprise; International audience; Globalisation and technology advancements have disrupted the organisational landscape and with the proliferation of new technology; risk management is fundamental to transforming the business especially considering the dynamic nature of the digital society organisations now exist in. However, the challenge faced by the enterprise risk management (ERM) function operating in such a dynamic and transformative environment, is the capability to continuously innovate, evolve and transform its risk management processes to meet the needs of the organisation. Questionnaire survey research examined the relative importance of 18 critical success factors for dynamic ERM. Factor analysis revealed that the appropriate grouping of the 18 critical success factors (CSFs) are ERM charter, ERM processes, and ERM business alignment. These findings should empower organisations to identify risk management processes influencing agility in the risk management practise applied.

Part 7: Digital Governance; International audience; This paper aims to investigate the process of personal data protection in Georgia within the frame of e-governance, focusing on available legal and technological protecting mechanisms, their practical usage and importance for realizing principles of good governance in the state. The scope of this research is defined by the protection of state databases containing citizen’s personal data. Its key legal and technological aspects are identified and analyzed. The potential of proper data protection to act as the enabler of e-governance services success is also evaluated. We explore the defense mechanisms of Georgian governmental entities by conducting interviews with seven experts from the Personal Data Protection Inspectorate and other public entities handling citizens’ data. We study citizens’ perception of data safety and the citizens’ knowledge of existing monitoring mechanisms through analysis of over 400 responses that we have received to our survey. We also analyze and assess the influence of these factors on the success of e-governance and its broad diffusion. Finally, guidelines and recommendations are formulated for raising citizens’ awareness on the data protection mechanisms to be used in future theoretical and practical considerations.

Part 1: E-Business; International audience; Most adults in industrialized countries now routinely check online reviews before selecting a product or service such as lodging. This reliance on online reviews can entice some hotel managers to pay for fraudulent reviews – either to boost their own property or to disparage their competitors. The detection of fraudulent reviews has been addressed by humans and by machine learning approaches yet remains a challenge. We conduct an empirical study in which we create fake reviews, merge them with verified reviews and then employ four methods (Naïve Bayes, SVMs, human computation and hybrid human-machine approaches) to discriminate the genuine reviews from the false ones. We find that overall a hybrid human-machine method works better than either human or machine-based methods for detecting fraud – provided the most salient features are chosen. Our process has implications for fraud detection across numerous domains, such as financial statements, insurance claims, and reporting clinical trials.

Part 12: Digital Innovation and Business Transformation; International audience; Open innovation ecosystems rely upon inter-organisational knowledge transfer to support co-creation. Despite the significance of this process, and an abundance of open innovation research, empirical investigation and discussion of diverse knowledge transfer conditions across open innovation ecosystems remains unaddressed within existing literature. Using a mixed-method approach, this study investigates how knowledge, firm, and partner-relationship characteristics affect the successful exchange of knowledge between ecosystem partners. Interpretive Structural Modelling was employed to ascertain expert opinions regarding the interrelations between the transfer conditions. The combinatory nature of these conditions, and their integration into solutions for success, was further explored utilizing fuzzy-set Qualitative Comparative Analysis. Results indicate that conditions for knowledge transfer success are highly interrelated and co-dependent. Limitations and implications are discussed.

Part 2: Big Data Analytics; International audience; The world changes and new digital solutions arise in a fast pace, requiring organizations to adapt fast to these changes and innovations. Consequently, the required skills and knowledge of employees to effectively contribute to the core business of an organization, are also subject to changes. Currently, Human Resource (HR) professionals cannot acquire a sufficient insight into organizations’ human capital in order to align it with the current and future core processes of the organization. To acquire the necessary insights and knowledge, organizations see great potential in harnessing the wealth of data about their human capital and core processes. Hence, organizations have a great interest in HR Analytics, which is a rising field on the intersection of HR and big data. Despite the interest, there are only few efforts that focus on HR Analytics and its implementations. In this paper, we analyze the characteristics of the current HR function, present a framework that systematically exploits HR data in order to improve the HR function, and describe our implementation of parts of the presented framework. As such, our framework and its implementations can be regarded as a first step towards conducting HR Analytics in practice.

Part 7: Digital Governance; International audience; Governments around the world are using information and communication technologies to offer both simple information portals and transactional services. A less than one-third of the electronic government (e-government) initiatives focused on the provision of transactional services and understanding on studies related to the adoption of such services using domain-specific adoption theories/models are scarce. Therefore, the objective of this study is to understand the adoption of transactional service system, i.e. ‘Pak-Identity’ by employing a domain-specific model, i.e. Unified Model of Electronic Government Adoption (UMEGA). A UMEGA model with four new constructs is validated using data gathered from 441 citizens from all over Pakistan. A survey was conducted among citizens using simple random sampling technique. The collected data were analyzed employing variance-based structure equation modelling, i.e. partial least squares technique in SmartPLS 3.0 to test the formulated hypotheses. Findings indicate that (1) facilitating conditions is the predictor of effort expectancy, (2) performance expectancy, trust, and herd behaviour are the predictors of attitude, (3) price value, grievance redressal, and attitude are the predictors of behavioural intention to use e-government service. Surprisingly, effort expectancy, facilitating conditions, social influence, and perceived risk are found to be the nonsignificant predictors of adoption of e-government service. Interestingly, new constructs and new relationships are exposed, i.e. trust and herd behaviour on attitude, and price value and grievance redressal on behavioural intention. Moreover, a 55% variance in effort expectancy, 65% variance in attitude, and 40% variance in behavioural intention to adopt e-government has been found. Implications for the academics and managers are also outlined.

Part 10: Security in Digital Environments; International audience; Despite high quality information systems security in place, organizations are vulnerable to cyber-attacks due to lapses in the human behavior. The present paper explores the importance of human factors in cyber security using an online survey data. It uses the work of Parson, Calic, Pattenson, Butavicius, McCormac and Zwaans [23] in measuring the human aspects of cyber security (leaving printouts, links from known source, website access, information in website, password complexity, links from known source, plugging USB in public places) and their linkages with the demographic factors (age, work experience, academic discipline, qualification, and place). ANOVA was used on a sample size of 165. It was found that demographic profile of employees and students significantly differ in their perception towards the cyber security. The paper has suggestions for information security awareness training programmes to handle the inadequacies.

Part 3: Open Science and Open Data; International audience; This paper focuses on demand forecasts for parking facilities. Our work utilizes open parking data for predictions. Several cities in Europe already publish this data continuously in the standardized DATEX II format. Traffic related information will become more ubiquitous in the future as all EU-member states must implement real-time traffic information services including parking status data since July 2017 implementing the EU directives 2010/40 and 2015/962. We demonstrate how to extract reliable and easily comprehensible forecast models for future-parking demand based on open data. These models find multiple use cases not only on a business planning level and for financial revenue forecasting but also to make traffic information systems more resilient to outages and to improve routing of drivers directing them to parking facilities with availability upon predicted arrival. Our approach takes into consideration that the data constitutes irregular time series and incorporates contextual information into the predictive models to obtain higher precision forecasts.

Part 3: Open Science and Open Data; International audience; Recently, the topic of research data management (RDM) has emerged at the forefront of Open Science. Funders and publishers posit new expectations on data management planning and transparent reporting of research. At the same time, laboratories rely upon undocumented files to record data, process results and submit manuscripts which hinders repeatable and replicable management of experimental resources. In this study, we design a forensic process to reconstruct and evaluate data management practices in scientific laboratories. The process we design is named Laboratory Forensics (LF) as it combines digital forensic techniques and the systematic study of experimental data. We evaluate the effectiveness and usefulness of Laboratory Forensics with laboratory members and data managers. Our preliminary evaluation indicates that LF is a useful approach for assessing data management practices. However, LF needs further developments to be integrated into the information systems of scientific laboratories.

Part 6: Social Media and Analytics; International audience; Ever since demonetization happened in India on November 8, 2016, there has been a steady improvement in the use of digital payments. The usage trend has become exponential in the last 15 months across the digital payment methods (DPMs) in India. Government of India is promoting this landmark change of Indian financial system through Digital India initiative. Both national and international corporates, established as well as startups are investing heavily in DPMs to promote their products and services among Indian consumers. The promotion of DPMs also includes the use of social media marketing. Social media (especially Twitter) is being extensively used by companies to make the consumer aware about their services and promote themselves. The consumers do get motivated to try and use their services but they also share their grudges on Twitter. This paper aims to analyze DPMs adoption in India using Twitter as a tool. This study collected 172996 tweets over a period of four months and analyzed using a mix of conventional as well novel social media analytics techniques. Our analysis highlights the critical factors that drive and inhibit the use of DPMs in India. Further, our state wise analysis clearly differentiates about DPM adoption rate from high to low in all states of India.

Part 6: Social Media and Analytics; International audience; Social media platforms are an inexpensive communication medium help to reach other users very quickly. The same benefit is also utilized by some mischievous users to post objectionable images and symbols to certain groups of people. This types of posts include cyber-aggression, cyberbullying, offensive content, and hate speech. In this work, we analyze images posted on online social media sites to hurt online users. In this research, we designed a deep learning based system to classify aggressive post from a non-aggressive post containing symbolic images. To show the effectiveness of our model, we created a dataset crawling images from Google search to query aggressive images. The validation shows promising results.

Part 1: E-Business; International audience; Companies and their brands initiated various Co-creation practices on social media. Co-creation improves value for both companies and their customers. This study explored the customer perspective of interest for participating in such Co-creation opportunities on Facebook. Drawing upon the Use and Gratification Theory we investigated the intention of customers to take part in Co-creation on Facebook. We related people’s Co-creation behaviour to the expectancy of satisfaction or reward for their actions. Customer Engagement is an additional concept that expresses the emotional attachment of customers to brands and companies. Underlying reasons were investigated why customers would consider taking part in Co-creation. The quantitative survey inquired customers about their expected Benefits, level of Customer Engagement and their intention for taking part in Co-creation with companies on Facebook. Our results showed that Customer Engagement can be considered as the most important predictor for the intention to Co-create instead of User Gratification. Hedonic Benefits are the most important drivers for User Gratification, playing a key role in the people’s intention for taking part in Co-creation practices. Overall, people’s intention for Co-creation on Facebook increases when they have a meaningful and pleasurable way of experiencing the companies’ products that concern them.

Part 6: Social Media and Analytics; International audience; Social media plays an important role in the digital transformation of businesses. This research provides a comprehensive analysis of the use of social media by b2b companies. The current study focuses on a number of aspects of social media such as: the effect of social media; social media tools; social media use; adoption of social media use and its barriers; social media strategies; and measuring the effectiveness of the use of social media. This research provides a valuable synthesis of the relevant literature on social media in the b2b context by analysing and discussing the key findings from existing research on social media. The findings of this study can be used as an informative framework on social media for both academics and practitioners.

Part 6: Social Media and Analytics; International audience; Interactions in open source communities are often informal, and enacted through online discussion forums. While discussion and associated sentiment is critical to sustaining open source communities, they have not been studied to date. To address this gap in knowledge, this study uses sentiment analytics to illuminate the frequency of 2,364 discursive manifestations of contradictions through the theoretical lens of Activity Theory (AT). The study contributes to current discourse on contradictions by demonstrating the importance of dialectical contradictions as a driving force for learning, change, and sustaining open source communities. Implications for research and practice provide opportunities for revising current business methods and practices, which inevitably have implications for a sustainable society in the 21st century.

Part 13: Online Communities; International audience; In this paper we present a virtual collaborative student project across two universities in the UK and Norway. The students involved were all Master students in Digital Innovation & Analytics and Digital Collaboration retrospectively. The project also had an industry partner, Cisco, and was set up as part of students’ learning in their corresponding universities. Five student-based Global virtual teams (GVTs) were formed. We draw on the experiences of these GVTs to gain better understanding of students’ experiences in dispersed collaboration giving particular focus on the leadership practices adopted in student-based GVTs. Our analysis of the GVT members’ experiences and reflections show that the way leadership was enacted had a role to play in the collaboration within the GVT. Overall, students appreciated being given the opportunity to be part of a globally dispersed project and were able to identify lessons learned and skills that they gained from the experience. Further, students were able to get practical experience in being part of virtual teams and to implement some of the ideas and approaches that they have learned from theory and in class discussions. For example, personal qualities that are central to success in virtual teams, i.e. communication skills, intercultural skills, interpersonal skills, methodological and technical skills, team working skills and leadership skills.

Part 9: Learning and Education; International audience; Studies show that the integration of ICT in education is suffering from some barriers at student, teacher, and university level. This also holds for multi-campus education, as ICT is one of the main ingredients for sharing education among campuses. In this study, through a pragmatic case study at the context of a Scandinavian multi-campus university and with the help of activity theory, we examined the challenges of offering a single-campus course to students at multiple campuses by using ICT for lecture sharing, without further adjustments. Data for this study is collected from different sources (teachers, teaching assistants, and students) through interviews and observations and from an online questionnaire. Our findings suggest that repurposing a single-campus course to a multi-campus one without having clear and pre-defined rules poses some challenges for the different stockholders and influences their relations, resulting in negative impacts on teaching and learning processes and teacher and students’ satisfaction. Therefore, we conclude that there should be careful planning with a clear set of rules, including technology training and preparation for teaching staff, students, and technical staff. Staff structure may also need to be modified to accommodate the additional needs for technical support.

Part 5: Smart Cities and Smart Homes; International audience; Smart city innovations can enhance the quality of citizens’ life through different smart technology management solutions, including resource management, data management, and software management. Nowadays, there are two primary proposals for smart technology management architectures in smart city environments, centralized, and distributed-to-centralized. The distributed-to-centralized schema architecture has several advantages. These advantages emerge from the contribution of distributed (e.g., Fog and cloudlet), and centralized (e.g., Cloud) technologies. For instance, decreasing network communication traffic and its latencies, improving data quality, and upgrading the security and privacy levels. In this paper, we develop our proposed Distributed-to-Centralized Data Management (D2C-DM) architecture and suggest novel contributions. First, we describe a new fully hierarchical software management architecture for smart cities based on Fog, cloudlet, and Cloud technologies. This Distributed-to-Centralized Software Management (D2C-SM) architecture can provide different software and services layers (including local, historical, and combined) through using distinct data types gathered from physical (e.g., sensors and smartphones) and non-physical (e.g., simulated data, and external databases) data sources in the smart city. Second, we envisage that our proposed D2C-SM can fit the software requirements of the Zero Emission Neighborhoods (ZEN) center. Thereafter, we use three different use cases of the ZEN center to depict the easy adaptation of our proposed ICT architecture, including D2C-SM and D2C-DM architectures.

Part 11: Modelling and Managing the Digital Enterprise; International audience; With the popularity of digital transformation programmes becoming widerspread, our study is motivated by a need to understand who has the responsibility for digital transformation across different types of organisations and industries. We take a specific focus on the Chief Digital Officer which has been considered the most fast growing executive role in the recent years. In particular, in this paper, we present a research in progress study that aims to examine the role of Chief Digital Officer in enabling digital transformation in traditional, pre-digital organisations, and to identify its impact on the IT department of these organisations. The empirical study is based on the qualitative approach with its main dataset deriving from a series of semi-structured interviews. The study is expected to have both theoretical and practical implications on the management of digital transformation.

Part 6: Social Media and Analytics; International audience; Technologies are changing how stakeholders, and investors access and capture data. Social Media has had a dramatic impact on how firms communicate with investors and stakeholders about their financial and sustainability reporting, giving them an edge over their competitors. The aim of this paper is to investigate the relationship between social media reporting and firm value of the GCC listed firms. To answer to research questions, the researchers collected cross-sectional data from a sample of 241 firms listed in the financial stock markets of the GCC for the year 2017. Additionally, an Index was used to calculate the total level of social media disclosure. The findings show that the 84% of firms in the GCC countries use social media, while 70% of these firms use SM for reporting. The results indicate that enhanced reporting levels through various social media channels significantly influence value of firms. These results have implications for GCC listed firms as it is important to examine how they can utilize social media to enhance their reporting process.