ALBERT

Description: As internet technologies make their way into developing areas, so too does the possibility of education and training being delivered to the people living in those previously unserved areas. The growing catalogue of free, high quality courseware, when combined with the newly acquired means of delivery, creates the potential for millions of people in the developing world to acquire a good education. Yet a good education obviously requires more than simply delivering information; students must also receive high quality feedback on their assessments. They must be told how their performance compares with the ideal, and be shown how to close the gap between the two. However, delivering high quality feedback is labor-intensive, and therefore expensive, and has long been recognized as a problematic issue by educators. This paper outlines a case study that uses a Learning Management System (LMS) to efficiently deliver detailed feedback that is informed by the principles of best practice. We make the case that the efficiencies of this method allow for large-scale courses with thousands of enrolments that are accessible to developing and developed areas alike. We explore the question; is computer-mediated feedback delivery efficient and effective and might it be applied to large-scale courses at low-cost?

Description: This paper proposes a discussion concerning the use of social media-related geographic information in the context of the strategic environmental assessment (SEA) of Sardinian Municipal masterplans (MMPs). We show that this kind of information improves, substantially, the SEA process since it provides planners, evaluators, and the local communities with information retrieved from social media that would have not been available otherwise. This information integrates authoritative data collection, which comes from official sources, and enlightens tastes and preferences of the users of services and infrastructure, and their expectations concerning their spatial organization. A methodological approach related to the collection of social media-related geographic information is implemented and discussed with reference to the urban context of the city of Cagliari (Sardinia, Italy). The results are very effective in terms of provision of information, which may possibly increase the spatial knowledge available for planning policy definition and implementation. In this perspective, this kind of information discloses opportunities for building analytical scenarios related to urban and regional planning and it offers useful suggestions for sustainable development based on tourism strategies.

Description: To monitor multiple environmental factors of henhouses in modern chicken farms, a henhouse online monitoring system based on wireless sensor network was developed using wireless sensor technology and computer network technology. Sensor data compensation and correction were designed to be achieved using software and data fitting methods, data reliable transmission achieved using a data loss recovery strategy, and data missing during monitoring addressed using a self-decision and online filling method. Operation test of the system showed that: The system was economic and reliable; it enabled wireless monitoring and Web display of the environmental factors of a henhouse; and the root mean square errors (RMSEs) between the estimated values from the self-decision and on-line filling method and experimental values of the four environmental factors were 0.1698, 3.0859, 77 and 0.094, respectively, indicative of high estimation accuracy. The system can provide support for modern management of henhouses and can be transplanted to related monitoring scenarios in the agricultural field.

Description: The self-organizing nature of the Mobile Ad hoc Networks (MANETs) provide a communication channel anywhere, anytime without any pre-existing network infrastructure. However, it is exposed to various vulnerabilities that may be exploited by the malicious nodes. One such malicious behavior is introduced by blackhole nodes, which can be easily introduced in the network and, in turn, such nodes try to crumble the working of the network by dropping the maximum data under transmission. In this paper, a new protocol is proposed which is based on the widely used Ad hoc On-Demand Distance Vector (AODV) protocol, Enhanced Secure Trusted AODV (ESTA), which makes use of multiple paths along with use of trust and asymmetric cryptography to ensure data security. The results, based on NS-3 simulation, reveal that the proposed protocol is effectively able to counter the blackhole nodes in three different scenarios.

Description: This paper is based on the experience of introducing wireless sensor networks (WSNs) into the building industry in Denmark and in a rural area of Greenland. There are very real advantages in the application of the technology and its consequences for the life cycle operation of the building sector. Sensor networks can be seen as an important part of the Internet of Things and may even constitute an Internet of Sensors, since the communication layers can differ from the Internet standards. The current paper describes the case for application, followed by a discussion of the observed adaptive advantages and consequences of the technology. Essentially, WSNs constitute a highly sophisticated technology that is more robust in a rural context due to its extremely simple installation procedures (plug and play) allowing the use of local less-skilled labour, and the possibility of reconfiguring and repurposing its use remotely.

Description: When a disaster hits a wide area, communication services for public use will be rendered unavailable. This will make it difficult to confirm the safety of people in the disaster area. A solution to this problem is to form delay/disruption tolerant networks (DTN) using mobile terminals of victims, those of rescuers, who serve as information carriers, and terminals (servers) in shelters. In this paper, we propose using a receiver-triggered handshake protocol for communication between these terminals. We have developed the bundle layer protocol for this handshake method. The proposed method has been implemented on a network simulator to build an evaluation environment. The disaster area has been modeled on an area around Shinjuku Station in Tokyo. Victims are randomly distributed in the area. We have compared the proposed method with Epidemic Routing and Spray and Wait in terms of the delivery rate at which messages reach their destinations, and the length of time taken for messages to reach their destinations. We have found that the delivery rate of the three methods are, more or less, the same, but that the proposed method is superior to the other two methods in terms of storage usage and battery consumption of terminals, and the number of bundles generated in the network.

Description: Big data makes cloud computing more and more popular in various fields. Video resources are very useful and important to education, security monitoring, and so on. However, issues of their huge volumes, complex data types, inefficient processing performance, weak security, and long times for loading pose challenges in video resource management. The Hadoop Distributed File System (HDFS) is an open-source framework, which can provide cloud-based platforms and presents an opportunity for solving these problems. This paper presents video resource management architecture based on HDFS to provide a uniform framework and a five-layer model for standardizing the current various algorithms and applications. The architecture, basic model, and key algorithms are designed for turning video resources into a cloud computing environment. The design was tested by establishing a simulation system prototype.

Description: The problem of optimal multicast routing in Wireless Mess Networks (WMNs) with Quality-of-Service (QoS) provisioning, which is Non-Deterministic Polynomial (NP)-complete, is studied in this paper. The existing algorithms are not very efficient or effective. In order to find an approximation optimal solution for WMNs in feasible time from source to the set of destination nodes, combining the previous deterministic algorithm with the well-known Minimum Path Cost Heuristic (MPH) algorithm, a novel multicast heuristic approximation (NMHA) algorithm with QoS provisioning is proposed in this paper to deal with it. The theoretical validations for the proposed algorithm are presented to show its performance and efficiency. After that, the random static networks with different destination nodes are evaluated. Simulations in these networks show that the proposed algorithm can achieve the approximate optimal solution with the approximation factor of 2(1 + ε)(1 − 1/q) and the time complexity of O(qmn2τK−1).

Description: In the recent years the progress in technology and the increasing availability of fast connections have produced a migration of functionalities in Information Technologies services, from static servers to distributed technologies. This article describes the main tools available on the market to perform Analytics as a Service (AaaS) using a cloud platform. It is also described a use case of IBM Watson Analytics, a cloud system for data analytics, applied to the following research scope: detecting the presence or absence of Heart Failure disease using nothing more than the electrocardiographic signal, in particular through the analysis of Heart Rate Variability. The obtained results are comparable with those coming from the literature, in terms of accuracy and predictive power. Advantages and drawbacks of cloud versus static approaches are discussed in the last sections.

Description: Instagram and WhatsApp are two social media and networking services introduced in 2010. They are currently subsidiaries of Facebook, Inc., California, USA. Using evidence from the published literature and case reports indexed in PubMed and other sources, we present an overview of the various applications of Instagram and WhatsApp in health and healthcare. We also briefly describe the main issues surrounding the uses of these two apps in health and medicine.

Description: Cost estimation is one of the most critical tasks for building construction project management. The existing building construction cost estimation methods of many countries, including China, require information from several sources, including material, labor, and equipment, and tend to be manual, time-consuming, and error-prone. To solve these problems, a building construction cost estimation model based on ontology representation and reasoning is established, which includes three major components, i.e., concept model ontology, work item ontology, and construction condition ontology. Using this model, the cost estimation information is modeled into OWL axioms and SWRL rules that leverage the semantically rich ontology representation to reason about cost estimation. Based on OWL axioms and SWRL rules, the cost estimation information can be translated into a set of concept models, work items, and construction conditions associated with the specific construction conditions. The proposed method is demonstrated in Protégé 3.4.8 through case studies based on the Measurement Specifications of Building Construction and Decoration Engineering taken from GB 50500-2013 (the Chinese national mandatory specifications). Finally, this research discusses the limitations of the proposed method and future research directions. The proposed method can help a building construction cost estimator extract information more easily and quickly.

Description: The information society is increasingly more dependent on Information Security Management Systems (ISMSs), and the availability of these kinds of systems is now vital for the development of Small and Medium-Sized Enterprises (SMEs). However, these companies require ISMSs that have been adapted to their special features, and which are optimized as regards the resources needed to deploy and maintain them. This article shows how important the security culture within ISMSs is for SMEs, and how the concept of security culture has been introduced into a security management methodology (MARISMA is a Methodology for “Information Security Management System in SMEs” developed by the Sicaman Nuevas Tecnologías Company, Research Group GSyA and Alarcos of the University of Castilla-La Mancha.) for SMEs. This model is currently being directly applied to real cases, thus allowing a steady improvement to be made to its implementation.

Description: Interoperability and social implication are two current challenges in the digital library (DL) context. To resolve the problem of interoperability, our work aims to find a relationship between the main metadata schemas. In particular, we want to formalize knowledge through the creation of a metadata taxonomy built with the analysis and the integration of existing schemas associated with DLs. We developed a method to integrate and combine Instagram metadata and hashtags. The final result is a taxonomy, which provides innovative metadata with respect to the classification of resources, as images of Instagram and the user-generated content, that play a primary role in the context of modern DLs. The possibility of Instagram to localize the photos inserted by users allows us to interpret the most relevant and interesting informative content for a specific user type and in a specific location and to improve access, visibility and searching of library content.

Description: Cloud computing systems enable clients to rent and share computing resources of third party platforms, and have gained widespread use in recent years. Numerous varieties of mobile, small-scale devices such as smartphones, red e-health devices, etc., across users, are connected to one another through the massive internetwork of vastly powerful servers on the cloud. While mobile devices store “private information” of users such as location, payment, health data, etc., they may also contribute “semi-public information” (which may include crowdsourced data such as transit, traffic, nearby points of interests, etc.) for data analytics. In such a scenario, a mobile device may seek to obtain the result of a computation, which may depend on its private inputs, crowdsourced data from other mobile devices, and/or any “public inputs” from other servers on the Internet. We demonstrate a new method of delegating real-world computations of resource-constrained mobile clients using an encrypted program known as the garbled circuit. Using the garbled version of a mobile client’s inputs, a server in the cloud executes the garbled circuit and returns the resulting garbled outputs. Our system assures privacy of the mobile client’s input data and output of the computation, and also enables the client to verify that the evaluator actually performed the computation. We analyze the complexity of our system. We measure the time taken to construct the garbled circuit as well as evaluate it for varying number of servers. Using real-world data, we evaluate our system for a practical, privacy preserving search application that locates the nearest point of interest for the mobile client to demonstrate feasibility.

Description: Internet of Things (IoT) seems a viable way to enable the Smart Cities of the future. iNUIT (Internet of Things for Urban Innovation) is a multi-year research program that aims to create an ecosystem that exploits the variety of data coming from multiple sensors and connected objects installed on the scale of a city, in order to meet specific needs in terms of development of new services (physical security, resource management, etc.). Among the multiple research activities within iNUIT, we present two projects: SmartCrowd and OpEc. SmartCrowd aims at monitoring the crowd’s movement during large events. It focuses on real-time tracking using sensors available in smartphones and on the use of a crowd simulator to detect possible dangerous scenarios. A proof-of-concept of the application has been tested at the Paléo Festival (Switzerland) showing the feasibility of the approach. OpEc (Optimisation de l’Eclairage public) aims at using IoT to implement dynamic street light management and control with the goal of reducing street light energy consumption while guaranteeing the same level of security of traditional illumination. The system has been tested during two months in a street in St-Imier (Switzerland) without interruption, validating its stability and resulting in an overall energy saving of about 56%.

Description: In the last 20 years, the convergence of different factors—the rise of the complexity of science, the “data deluge” and the advances in information technologies—triggered a paradigm shift in the way we understand complex social systems and their evolution. Beyond shedding new light onto social dynamics, the emerging research area of Computational Social Science (CSS) is providing a new rationale for a more scientifically-grounded and effective policy design. The paper discusses the opportunities potentially deriving from the intersection between policy design issues and CSS methods. After a general introduction to the limits of traditional policy-making and a brief review of the most promising CSS methodologies, the work deals with way in which the insights potentially offered by CSS can concretely flow in policy choices. The attention is focused, to this end, on the legal mechanisms regulating the formulation and the evaluation of public policies. Our goal is two-fold: sketch how the project of a “smart society” is connected to the evolution of social sciences and emphasize the need for change in the way in which public policies are conceived of, designed and implemented.

Description: This paper presents two proposals for the analysis of the complexity of the Cyber security Ecosystem of Colombia (CEC). This analysis shows the available knowledge about entities engaged in cyber security in Colombia and the relationships between them, which allow an understanding of the synergy between the different existing components. The complexity of the CEC is detailed from the view of the Influence Diagram of System Dynamics and the Domain Diagram of Software Engineering. The resulting model makes cyber security evident as a strategic component of national security.

Description: Society is increasingly dependent on Information Security Management Systems (ISMS), and having these kind of systems has become vital for the development of Small and Medium-Sized Enterprises (SMEs). However, these companies require ISMS that have been adapted to their special features and have been optimized as regards the resources needed to deploy and maintain them, with very low costs and short implementation periods. This paper discusses the different cycles carried out using the ‘Action Research (AR)’ method, which have allowed the development of a security management methodology for SMEs that is able to automate processes and reduce the implementation time of the ISMS.

Description: Our increasing dependence on information technologies and autonomous systems has escalated international concern for information- and cyber-security in the face of politically, socially and religiously motivated cyber-attacks. Information warfare tactics that interfere with the flow of information can challenge the survival of individuals and groups. It is increasingly important that both humans and machines can make decisions that ensure the trustworthiness of information, communication and autonomous systems. Subsequently, an important research direction is concerned with modelling decision-making processes. One approach to this involves modelling decision-making scenarios as games using game theory. This paper presents a survey of information warfare literature, with the purpose of identifying games that model different types of information warfare operations. Our contribution is a systematic identification and classification of information warfare games, as a basis for modelling decision-making by humans and machines in such scenarios. We also present a taxonomy of games that map to information warfare and cyber crime problems as a precursor to future research on decision-making in such scenarios. We identify and discuss open research questions including the role of behavioural game theory in modelling human decision making and the role of machine decision-making in information warfare scenarios.

Description: A wide range of IDS implementations with anomaly detection modules have been deployed. In general, those modules depend on intrusion knowledge databases, such as Knowledge Discovery Dataset (KDD99), Center for Applied Internet Data Analysis (CAIDA) or Community Resource for Archiving Wireless Data at Dartmouth (CRAWDAD), among others. Once the database is analyzed and a machine learning method is employed to generate detectors, some classes of new detectors are created. Thereafter, detectors are supposed to be deployed in real network environments in order to achieve detection with good results for false positives and detection rates. Since the traffic behavior is quite different according to the user’s network activities over available services, restrictions and applications, it is supposed that behavioral-based detectors are not well suited to all kind of networks. This paper presents the differences of detection results between some network scenarios by applying traditional detectors that were calculated with artificial neural networks. The same detector is deployed in different scenarios to measure the efficiency or inefficiency of static training detectors.

Description: Quality web service discovery requires narrowing the search space from an overwhelming set of services down to the most relevant ones, while matching the consumer’s request. Today, the ranking of services only considers static attributes or snapshots of current attribute values, resulting in low-quality search results. To satisfy the user’s need for timely, well-chosen web services, we ought to consider quality of service attributes. The problem is that dynamic attributes can be difficult to measure, frequently fluctuate, are context-sensitive and depend on environmental factors, such as network availability at query time. In this paper, we propose the Dynamis algorithm to address these challenges effectively. Dynamis is based on well-established database techniques, such as skyline and aggregation. We illustrate our approach using observatory telescope web services and experimentally evaluate it using stock market data. In our evaluation, we show significant improvement in service selection over existing techniques.

Description: To improve the learning of basic concepts in molecular biology of an undergraduate science class, a pedagogical tool was developed, consisting of learning objectives listed at the end of each lecture and answers to those objectives made available as videos online. The aim of this study was to determine if the pedagogical tool was used by students as instructed, and to explore students’ perception of its usefulness. A combination of quantitative survey data and measures of online viewing was used to evaluate the usage of the pedagogical practice. A total of 77 short videos linked to 11 lectures were made available to 71 students, and 64 completed the survey. Using online tracking tools, a total of 7046 views were recorded. Survey data indicated that most students (73.4%) accessed all videos, and the majority (98.4%) found the videos to be useful in assisting their learning. Interestingly, approximately half of the students (53.1%) always or most of the time used the pedagogical tool as recommended, and consistently answered the learning objectives before watching the videos. While the proposed pedagogical tool was used by the majority of students outside the classroom, only half used it as recommended limiting the impact on students’ involvement in the learning of the material presented in class.

Description: In this explorative study, we examine the economy and transaction network of the decentralized digital currency Bitcoin during the first four years of its existence. The objective is to develop insights into the evolution of the Bitcoin economy during this period. For this, we establish and analyze a novel integrated dataset that enriches data from the Bitcoin blockchain with off-network data such as business categories and geo-locations. Our analyses reveal the major Bitcoin businesses and markets. Our results also give insights on the business distribution by countries and how businesses evolve over time. We also show that there is a gambling network that features many very small transactions. Furthermore, regional differences in the adoption and business distribution could be found. In the network analysis, the small world phenomenon is investigated and confirmed for several subgraphs of the Bitcoin network.

Description: Feature extraction methods have been used to extract location features for indoor positioning in wireless local area networks. However, existing methods, such as linear discriminant analysis and principal component analysis, all suffer from the multimodal property of signal distribution. This paper proposes a novel method, based on enhanced local fisher discriminant analysis (LFDA). First, LFDA is proposed to extract discriminative location features. It maximizes between-class separability while preserving within-class local structure of signal space, thereby guaranteeing maximal discriminative information involved in positioning. Then, the generalization ability of LFDA is further enhanced using signal perturbation, which generates more number of representative training samples. Experimental results in realistic indoor environment show that, compared with previous feature extraction methods, the proposed method reduces the mean and standard deviation of positing error by 23.9% and 33.0%, respectively.

Description: Users share vast amounts of personal information online, but are they fully aware of what information they are sharing and with whom? In this paper, we focused on Facebook apps and set out to understand how concerned users are about privacy and how well-informed they are about what personal data apps can access. We found that initially, subjects were generally under-informed about what data apps could access from their profiles. After viewing additional information about these permissions, subjects’ concern about privacy on Facebook increased. Subjects’ understanding of what data apps were able to access increased, although even after receiving explicit information on the topic, many subjects still did not fully understand the extent to which apps could access their data.

Description: Over the years, the cellular mobile network has evolved from a wireless plain telephone system to a very complex system providing telephone service, Internet connectivity and many interworking capabilities with other networks. Its air interface performance has increased drastically over time, leading to high throughput and low latency. Changes to the core network, however, have been slow and incremental, with increased complexity worsened by the necessity of backwards-compatibility with older-generation systems such as the Global System for Mobile communication (GSM). In this paper, a new virtualized Peer-to-Peer (P2P) core network architecture is presented. The key idea of our approach is that each user is assigned a private virtualized copy of the whole core network. This enables a higher degree of security and novel services that are not possible in today’s architecture. We describe the new architecture, focusing on its main elements, IP addressing, message flows, mobility management, and scalability. Furthermore, we will show some significant advantages this new architecture introduces. Finally, we investigate the performance of our architecture by analyzing voice-call traffic available in a database of a large U.S. cellular network provider.

Description: The ongoing digital transformation has the potential to revolutionize nearly all industrial manufacturing processes. However, its concrete requirements and implications are still not sufficiently investigated. In order to establish a common understanding, a multitude of initiatives have published guidelines, reference frameworks and specifications, all intending to promote their particular interpretation of the Industrial Internet of Things (IIoT). As a result of the inconsistent use of terminology, heterogeneous structures and proposed processes, an opaque landscape has been created. The consequence is that both new users and experienced experts can hardly manage to get an overview of the amount of information and publications, and make decisions on what is best to use and to adopt. This work contributes to the state of the art by providing a structured analysis of existing reference frameworks, their classifications and the concerns they target. We supply alignments of shared concepts, identify gaps and give a structured mapping of regarded concerns at each part of the respective reference architectures. Furthermore, the linking of relevant industry standards and technologies to the architectures allows a more effective search for specifications and guidelines and supports the direct technology adoption.

Description: Google’s Material Design, created in 2014, led to the extended application of floating action buttons (FAB) in user interfaces of web pages and mobile applications. FAB’s roll is to trigger an activity either on the present screen, or it can play out an activity that makes another screen. A few specialists in user experience (UX) and user interface (UI) design are sceptical regarding the usability of FAB in the interfaces of both web pages and mobile applications. They claim that the use of FAB easily distracts users and that it interferes with using other important functions of the applications, and it is unusable in applications designed for iOS systems. The aim of this paper is to investigate by an experiment the quality of experience (QoE) of a static and animated FAB and compare it to the toolbar alternative. The experimental results of different testing methods rejected the hypothesis that the usage and animation of this UI element has a positive influence on the application usability. However, its static and animated utilization enhanced the ratings of hedonic and aesthetic features of the user experience, justifying the usage of this type of button.

Description: Service Level Agreements are employed to set availability commitments in cloud services. When a violation occurs as in an outage, cloud providers may be called to compensate customers for the losses incurred. Such compensation may be so large as to erode cloud providers’ profit margins. Insurance may be used to protect cloud providers against such a danger. In this paper, closed formulas are provided through the expected utility paradigm to set the insurance premium under different outage models and QoS metrics (no. of outages, no. of long outages, and unavailability). When the cloud service is paid through a fixed fee, we also provide the maximum unit compensation that a cloud provider can offer so as to meet constraints on its profit loss. The unit compensation is shown to vary approximately as the inverse square of the service fee.

Description: In semi-autonomous robot conferencing, not only the operator controls the robot, but the robot itself also moves autonomously. Thus, it can modify the operator’s movement (e.g., adding social behaviors). However, the sense of agency, that is, the degree of feeling that the movement of the robot is the operator’s own movement, would decrease if the operator is conscious of the discrepancy between the teleoperation and autonomous behavior. In this study, we developed an interface to control the robot head by using an eye tracker. When the robot autonomously moves its eye-gaze position, the interface guides the operator’s eye movement towards this autonomous movement. The experiment showed that our interface can maintain the sense of agency, because it provided the illusion that the autonomous behavior of a robot is directed by the operator’s eye movement. This study reports the conditions of how to provide this illusion in semi-autonomous robot conferencing.

Description: Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.

Description: Military named entity recognition (MNER) is one of the key technologies in military information extraction. Traditional methods for the MNER task rely on cumbersome feature engineering and specialized domain knowledge. In order to solve this problem, we propose a method employing a bidirectional long short-term memory (BiLSTM) neural network with a self-attention mechanism to identify the military entities automatically. We obtain distributed vector representations of the military corpus by unsupervised learning and the BiLSTM model combined with the self-attention mechanism is adopted to capture contextual information fully carried by the character vector sequence. The experimental results show that the self-attention mechanism can improve effectively the performance of MNER task. The F-score of the military documents and network military texts identification was 90.15% and 89.34%, respectively, which was better than other models.

Description: In an era of accelerating digitization and advanced big data analytics, harnessing quality data and insights will enable innovative research methods and management approaches. Among others, Artificial Intelligence Imagery Analysis has recently emerged as a new method for analyzing the content of large amounts of pictorial data. In this paper, we provide background information and outline the application of Artificial Intelligence Imagery Analysis for analyzing the content of large amounts of pictorial data. We suggest that Artificial Intelligence Imagery Analysis constitutes a profound improvement over previous methods that have mostly relied on manual work by humans. In this paper, we discuss the applications of Artificial Intelligence Imagery Analysis for research and practice and provide an example of its use for research. In the case study, we employed Artificial Intelligence Imagery Analysis for decomposing and assessing thumbnail images in the context of marketing and media research and show how properly assessed and designed thumbnail images promote the consumption of online videos. We conclude the paper with a discussion on the potential of Artificial Intelligence Imagery Analysis for research and practice across disciplines.

Description: The exorbitant increase in the computational complexity of modern video coding standards, such as High Efficiency Video Coding (HEVC), is a compelling challenge for resource-constrained consumer electronic devices. For instance, the brute force evaluation of all possible combinations of available coding modes and quadtree-based coding structure in HEVC to determine the optimum set of coding parameters for a given content demand a substantial amount of computational and energy resources. Thus, the resource requirements for real time operation of HEVC has become a contributing factor towards the Quality of Experience (QoE) of the end users of emerging multimedia and future internet applications. In this context, this paper proposes a content-adaptive Coding Unit (CU) size selection algorithm for HEVC intra-prediction. The proposed algorithm builds content-specific weighted Support Vector Machine (SVM) models in real time during the encoding process, to provide an early estimate of CU size for a given content, avoiding the brute force evaluation of all possible coding mode combinations in HEVC. The experimental results demonstrate an average encoding time reduction of 52.38%, with an average Bjøntegaard Delta Bit Rate (BDBR) increase of 1.19% compared to the HM16.1 reference encoder. Furthermore, the perceptual visual quality assessments conducted through Video Quality Metric (VQM) show minimal visual quality impact on the reconstructed videos of the proposed algorithm compared to state-of-the-art approaches.

Description: A crowdsourcing contest is one of the most popular modes of crowdsourcing and is also an important tool for an enterprise to implement open innovation. The solvers’ active participation is one of the major reasons for the success of crowdsourcing contests. Research on solvers’ participation behavior is helpful in understanding the sustainability and incentives of solvers’ participation in the online crowdsourcing platform. So, how to attract more solvers to participate and put in more effort is the focus of researchers. In this regard, previous studies mainly used the submission quantity to measure solvers’ participation behavior and lacked an effective measure on the degree of participation effort expended by a solver. For the first time, we use solvers’ participation time as a dependent variable to measure their effort in a crowdsourcing contest. Thus, we incorporate participation time into the solver’s participation research. With the data from Taskcn.com, we analyze how participation time is affected four key factors including task design, task description, task process, and environment, respectively. We found that, first, for task design, higher task rewards will attract solvers to invest more time in the participation process and the relationship between participation time and task duration is inverted U-shaped. Second, for task description, the length of the task description has a negative impact on participation time and the task description attachment will positively influence the participation time. Third, for the task process, communication and supplementary explanations in a crowdsourcing process positively affect participation time. Fourth, for environmental factors, the task density of the crowdsourcing platform and the market price of all crowdsourcing contests have respectively negative and positive effects on participation time.

Description: The growing demand on video streaming services increasingly motivates the development of a reliable and accurate models for the assessment of Quality of Experience (QoE). In this duty, human-related factors which have significant influence on QoE play a crucial role. However, the complexity caused by multiple effects of those factors on human perception has introduced challenges on contemporary studies. In this paper, we inspect the impact of the human-related factors, namely perceptual factors, memory effect, and the degree of interest. Based on our investigation, a novel QoE model is proposed that effectively incorporates those factors to reflect the user’s cumulative perception. Evaluation results indicate that our proposed model performed excellently in predicting cumulative QoE at any moment within a streaming session.

Description: The main scope of the presented research was the development of an innovative product for the management of city parking lots. Our application will ensure the implementation of the Smart City concept by using computer vision and communication platforms, which enable the development of new integrated digital services. The use of video cameras could simplify and lower the costs of parking lot controls. In the aim of parking space detection, an aggregated decision was proposed, employing various metrics, computed over a sliding window interval provided by the camera. The history created over 20 images provides an adaptive method for background and accurate detection. The system has shown high robustness in two benchmarks, achieving a recognition rate higher than 93%.

Description: The application of blockchain technology to the energy sector promises to derive new operating models focused on local generation and sustainable practices, which are driven by peer-to-peer collaboration and community engagement. However, real-world energy blockchains differ from typical blockchain networks insofar as they must interoperate with grid infrastructure, adhere to energy regulations, and embody engineering principles. Naturally, these additional dimensions make real-world energy blockchains highly dependent on the participation of grid operators, engineers, and energy providers. Although much theoretical and proof-of-concept research has been published on energy blockchains, this research aims to establish a lens on real-world projects and implementations that may inform the alignment of academic and industry research agendas. This research classifies 131 real-world energy blockchain initiatives to develop an understanding of how blockchains are being applied to the energy domain, what type of failure rates can be observed from recently reported initiatives, and what level of technical and theoretical details are reported for real-world deployments. The results presented from the systematic analysis highlight that real-world energy blockchains are (a) growing exponentially year-on-year, (b) producing relatively low failure/drop-off rates (~7% since 2015), and (c) demonstrating information sharing protocols that produce content with insufficient technical and theoretical depth.

Description: The spectral efficiency of wireless networks can be significantly improved by exploiting spatial multiplexing techniques known as multi-user MIMO. These techniques enable the allocation of multiple users to the same time-frequency block, thus reducing the interference between users. There is ample evidence that user groupings can have a significant impact on the performance of spatial multiplexing. The situation is even more complex when the data packets have priority and deadlines for delivery. Hence, combining packet queue management and beamforming would considerably enhance the overall system performance. In this paper, we propose a combination of beamforming and scheduling to improve the overall performance of multi-user MIMO systems in realistic conditions where data packets have both priority and deadlines beyond which they become obsolete. This method dubbed Reward Per Second (RPS), combines advanced matrix factorization at the physical layer with recently-developed queue management techniques. We demonstrate the merits of the this technique compared to other state-of-the-art scheduling methods through simulations.

Description: An exemplary paradigm of how an AI can be a disruptive technological paragon via the utilization of blockchain comes straight from the world of deep learning. Data scientists have long struggled to maintain the quality of a dataset for machine learning by an AI entity. Datasets can be very expensive to purchase, as, depending on both the proper selection of the elements and the homogeneity of the data contained within, constructing and maintaining the integrity of a dataset is difficult. Blockchain as a highly secure storage medium presents a technological quantum leap in maintaining data integrity. Furthermore, blockchain’s immutability constructs a fruitful environment for creating high quality, permanent and growing datasets for deep learning. The combination of AI and blockchain could impact fields like Internet of things (IoT), identity, financial markets, civil governance, smart cities, small communities, supply chains, personalized medicine and other fields, and thereby deliver benefits to many people.

Description: With the development of artificial intelligence, machine learning algorithms and deep learning algorithms are widely applied to attack detection models. Adversarial attacks against artificial intelligence models become inevitable problems when there is a lack of research on the cross-site scripting (XSS) attack detection model for defense against attacks. It is extremely important to design a method that can effectively improve the detection model against attack. In this paper, we present a method based on reinforcement learning (called RLXSS), which aims to optimize the XSS detection model to defend against adversarial attacks. First, the adversarial samples of the detection model are mined by the adversarial attack model based on reinforcement learning. Secondly, the detection model and the adversarial model are alternately trained. After each round, the newly-excavated adversarial samples are marked as a malicious sample and are used to retrain the detection model. Experimental results show that the proposed RLXSS model can successfully mine adversarial samples that escape black-box and white-box detection and retain aggressive features. What is more, by alternately training the detection model and the confrontation attack model, the escape rate of the detection model is continuously reduced, which indicates that the model can improve the ability of the detection model to defend against attacks.

Description: The Internet of Things (IoT) is increasingly part of daily life. However, the development of IoT applications still faces many problems, such as heterogeneity, complex management, and other difficulties. In this paper, first, the open source technologies of IoT are surveyed. We compare these technologies from the point of view of different levels of technical requirements, such as device management, data management, communication, intelligent data processing, security and privacy protection; we also look at requirements of application development and deployment. Second, an IoT integrated development platform architecture for IoT applications based on open source ecosystem is proposed and evaluated in an industrial setting. We applied P2P technology to distributed resource management and blockchain-based smart contract mechanics for resource billing management. The results show that the IoT gateway based on an open source ecosystem had a stable and reliable system performance with a certain data size and concurrency scale. These conditions satisfy the application requirements of the IoT in most sensing environments.

Description: Presently, we are observing an explosion of data that need to be stored and processed over the Internet, and characterized by large volume, velocity and variety. For this reason, software developers have begun to look at NoSQL solutions for data storage. However, operations that are trivial in traditional Relational DataBase Management Systems (DBMSs) can become very complex in NoSQL DBMSs. This is the case of the join operation to establish a connection between two or more DB structures, whose construct is not explicitly available in many NoSQL databases. As a consequence, the data model has to be changed or a set of operations have to be performed to address particular queries on data. Thus, open questions are: how do NoSQL solutions work when they have to perform join operations on data that are not natively supported? What is the quality of NoSQL solutions in such cases? In this paper, we deal with such issues specifically considering one of the major NoSQL document oriented DB available on the market: MongoDB. In particular, we discuss an approach to perform join operations at application layer in MongoDB that allows us to preserve data models. We analyse performance of the proposes approach discussing the introduced overhead in comparison with SQL-like DBs.

Description: The goal of the present research is to contribute to the detection of tax fraud concerning personal income tax returns (IRPF, in Spanish) filed in Spain, through the use of Machine Learning advanced predictive tools, by applying Multilayer Perceptron neural network (MLP) models. The possibilities springing from these techniques have been applied to a broad range of personal income return data supplied by the Institute of Fiscal Studies (IEF). The use of the neural networks enabled taxpayer segmentation as well as calculation of the probability concerning an individual taxpayer’s propensity to attempt to evade taxes. The results showed that the selected model has an efficiency rate of 84.3%, implying an improvement in relation to other models utilized in tax fraud detection. The proposal can be generalized to quantify an individual’s propensity to commit fraud with regards to other kinds of taxes. These models will support tax offices to help them arrive at the best decisions regarding action plans to combat tax fraud.

Description: Gender role norms have been widely studied in the offline partner violence context. Different studies have indicated that internalizing these norms was associated with dating violence. However, very few research works have analyzed this relation in forms of aggression against partners and former partners using information and communication technologies (ICT). The objective of the present study was to examine the co-occurrence of cyber dating abuse by analyzing the extent to which victimization and perpetration overlap, and by analyzing the differences according to conformity to the masculine gender norms between men who are perpetrators or victims of cyber dating abuse. The participants were 614 male university students, and 26.5% of the sample reported having been a victim and perpetrator of cyber dating abuse. Nonetheless, the regression analyses did not reveal any statistically significant association between conformity to masculine gender norms and practicing either perpetration or victimization by cyber dating abuse.

Description: In recent years, the laboratory security of universities has become an important issue for students and devices. To solve this security issue, this paper proposes an intelligent monitoring system to realize environment detection in university laboratories. The main purpose of this system is to monitor the laboratory environment data in time and improve the laboratory inspection efficiency. The system consists of a single chip microcomputer, which is the core of this system, a sensor function module and GPRS wireless communication, realizing data monitoring and short message warning. Therefore, three features, front-end data acquisition, data wireless transmission and a security alarm, are achieved by the proposed system. The real experiments show that front-end data acquisition is effective, data transmission is reliable, and the alarm message is received in time. Furthermore, the system, with the modified function modules, can be used in other scenarios to detect environments, and thus has a significant applied value in other areas.

Description: Optical access networks, metro networks and mobile data networks are facing rapidly evolving demands, not only is it essential to satisfy the unyielding need for increased user bandwidths, but future networks must also support the growing wide variation in traffic dynamics and characteristics, due to various emerging technologies, such as cloud-based services, the Internet-of-Things (IoT) and 5G mobile systems, and due to growing trends, such as the proliferation of mobile devices and the rapidly increasing popularity of video-on-demand services. To be cost-effective and commercially sustainable, future optical networks must offer features, such as, dynamic reconfigurability, highly efficient use of network resources, elastic bandwidth provisioning with fine granularity, network sliceabilty and software defined networking (SDN). To meet these requirements Cloud Access Networks (CANs) are proposed which require a number of flexible, adaptive and reconfigurable networking elements. By exploiting digital signal processing (DSP) we have proposed a digital orthogonal filter-based multiplexing technique to implement CANs with multiplexed, independent optical channels at the wavelength, sub-wavelength, and orthogonal sub-band levels. This paper reviews the overall CAN concept, the operating principles of the various CAN network elements and presents an overview of the research work we have undertaken in order to validate the feasibility of the proposed technologies which includes real-time DSP-based demonstrations.

Description: Recurrent neural network (RNN) has achieved remarkable success in sequence labeling tasks with memory requirement. RNN can remember previous information of a sequence and can thus be used to solve natural language processing (NLP) tasks. Named entity recognition (NER) is a common task of NLP and can be considered a classification problem. We propose a bidirectional long short-term memory (LSTM) model for this entity recognition task of the Arabic text. The LSTM network can process sequences and relate to each part of it, which makes it useful for the NER task. Moreover, we use pre-trained word embedding to train the inputs that are fed into the LSTM network. The proposed model is evaluated on a popular dataset called “ANERcorp.” Experimental results show that the model with word embedding achieves a high F-score measure of approximately 88.01%.

Description: In this paper, a printed monopole antenna design for WiMAX/WLAN applications in cable-free self-positioning seismograph nodes is proposed. Great improvements were achieved in miniaturizing the antenna and in widening the narrow bandwidth of the high-frequency band. The antenna was fed by a microstrip gradient line and consisted of a triangle, an inverted-F shape, and an M-shaped structure, which was rotated 90° counterclockwise to form a surface-radiating patch. This structure effectively widened the operating bandwidth of the antenna. Excitation led to the generation of two impedance bands of 2.39–2.49 and 4.26–7.99 GHz for a voltage standing wave ratio of less than 2. The two impedance bandwidths were 100 MHz, i.e., 4.08% relative to the center frequency of 2.45 GHz, and 3730 MHz, i.e., 64.31% relative to the center frequency of 5.80 GHz, covering the WiMAX high-frequency band (5.25–5.85 GHz) and the WLAN band (2.4/5.2/5.8). This article describes the design details of the antenna and presents the results of both simulations and experiments that show good agreement. The proposed antenna meets the field-work requirements of cable-less seismograph nodes.

Description: To address the continuous growth in high-speed ubiquitous access required by residential users and enterprises, Telecommunication operators must upgrade their networks to higher data rates. For optical fiber access networks that directly connect end users to metro/regional network, capacity upgrade must be done in a cost- and energy-efficient manner. 40 Gb/s is the possible lane rate for the next generation passive optical networks (NG-PONs). Ideally, existing 10 G PON components could be reused to support 40 Gb/s lane-rate NG-PON transceiver, which requires efficient modulation format and digital signal processing (DSP) to alleviate the bandwidth limitation and fiber dispersion. The major contribution of this work is to offer insight performance comparisons of 40 Gb/s lane rate electrical three level Duobinary, optical Duobinary, and four-level pulse amplitude modulation (PAM-4) for incorporating low complex DSPs, including linear and nonlinear Volterra equalization, as well as maximum likelihood sequence estimation. Detailed analysis and comparison of the complexity of various DSP algorithms are performed. Transceiver bandwidth optimization is also undertaken. The results show that the choices of proper modulation format and DSP configuration depend on the transmission distances of interest.

Description: The rapid development of distributed technology has made it possible to store and query massive trajectory data. As a result, a variety of schemes for big trajectory data management have been proposed. However, the factor of data transmission is not considered in most of these, resulting in a certain impact on query efficiency. In view of that, we present THBase, a coprocessor-based scheme for big trajectory data management in HBase. THBase introduces a segment-based data model and a moving-object-based partition model to solve massive trajectory data storage, and exploits a hybrid local secondary index structure based on Observer coprocessor to accelerate spatiotemporal queries. Furthermore, it adopts certain maintenance strategies to ensure the colocation of relevant data. Based on these, THBase designs node-locality-based parallel query algorithms by Endpoint coprocessor to reduce the overhead caused by data transmission, thus ensuring efficient query performance. Experiments on datasets of ship trajectory show that our schemes can significantly outperform other schemes.

Description: In this paper, we present an analysis of the mining process of two popular assets, Bitcoin and gold. The analysis highlights that Bitcoin, more specifically its underlying technology, is a “safe haven” that allows facing the modern environmental challenges better than gold. Our analysis emphasizes that crypto-currencies systems have a social and economic impact much smaller than that of the traditional financial systems. We present an analysis of the several stages needed to produce an ounce of gold and an artificial agent-based market model simulating the Bitcoin mining process and allowing the quantification of Bitcoin mining costs. In this market model, miners validate the Bitcoin transactions using the proof of work as the consensus mechanism, get a reward in Bitcoins, sell a fraction of them to cover their expenses, and stay competitive in the market by buying and divesting hardware units and adjusting their expenses by turning off/on their machines according to the signals provided by a technical analysis indicator, the so-called relative strength index.

Description: In recent years, almost all of the current top-performing object detection networks use CNN (convolutional neural networks) features. State-of-the-art object detection networks depend on CNN features. In this work, we add feature fusion in the object detection network to obtain a better CNN feature, which incorporates well deep, but semantic, and shallow, but high-resolution, CNN features, thus improving the performance of a small object. Also, the attention mechanism was applied to our object detection network, AF R-CNN (attention mechanism and convolution feature fusion based object detection), to enhance the impact of significant features and weaken background interference. Our AF R-CNN is a single end to end network. We choose the pre-trained network, VGG-16, to extract CNN features. Our detection network is trained on the dataset, PASCAL VOC 2007 and 2012. Empirical evaluation of the PASCAL VOC 2007 dataset demonstrates the effectiveness and improvement of our approach. Our AF R-CNN achieves an object detection accuracy of 75.9% on PASCAL VOC 2007, six points higher than Faster R-CNN.

Description: Convolutional Neural Networks (CNNs) are brain-inspired computational models designed to recognize patterns. Recent advances demonstrate that CNNs are able to achieve, and often exceed, human capabilities in many application domains. Made of several millions of parameters, even the simplest CNN shows large model size. This characteristic is a serious concern for the deployment on resource-constrained embedded-systems, where compression stages are needed to meet the stringent hardware constraints. In this paper, we introduce a novel accuracy-driven compressive training algorithm. It consists of a two-stage flow: first, layers are sorted by means of heuristic rules according to their significance; second, a modified stochastic gradient descent optimization is applied on less significant layers such that their representation is collapsed into a constrained subspace. Experimental results demonstrate that our approach achieves remarkable compression rates with low accuracy loss (〈1%).

Description: We propose a new framework to improve the engagement of medical practitioners in a planned e-training platform for the successful identification and effective management of presumptive cases of tuberculosis (TB) in Pakistan. Our work is aligned with the World Health Organization’s TB-DOTS (Directly Observed Treatment Short-course) strategy for promoting the effective management of tuberculosis. We start by presenting previous work done at Mercy Corps Pakistan for training medical practitioners, then present the results of a recent survey we administered to a random sample of medical practitioners in Pakistan to learn about their requirements and readiness to embrace a new e-training platform and methodology. Informed by feedback from the survey, we formulated a detailed requirement analysis of the five key learning areas (or phases) that we believe are fundamental to the success of a TB e-training platform. Moreover, survey results revealed that an on-spot, on-demand, and competency-based learning tool can potentially improve the engagement of medical practitioners in the process. Building on the insights gained from the survey, we finally describe our initial UX (user experience) prototypes for phase 1, which corresponds to the identification of presumptive tuberculosis cases.

Description: E-commerce is becoming more and more the main instrument for selling goods to the mass market. This led to a growing interest in algorithms and techniques able to predict products future prices, since they allow us to define smart systems able to improve the quality of life by suggesting more affordable goods and services. The joint use of time series, reputation and sentiment analysis clearly represents one important approach to this research issue. In this paper we present Price Probe, a suite of software tools developed to perform forecasting on products’ prices. Its primary aim is to predict the future price trend of products generating a customized forecast through the exploitation of autoregressive integrated moving average (ARIMA) model. We experimented the effectiveness of the proposed approach on one of the biggest E-commerce infrastructure in the world: Amazon. We used specific APIs and dedicated crawlers to extract and collect information about products and their related prices over time and, moreover, we extracted information from social media and Google Trends that we used as exogenous features for the ARIMA model. We fine-estimated ARIMA’s parameters and tried the different combinations of the exogenous features and noticed through experimental analysis that the presence of Google Trends information significantly improved the predictions.

Description: The fifth generation (5G) of wireless communication systems is considered the key technology to enable a wide range of application scenarios and the effective spreading of the smart city concept. Vertical business use cases, specifically designed for the future 5G city, will have a strong economical and social impact. For this reason, ongoing 5G field trials have to test newly deployed technologies as well as the capability of 5G to create a new digital economy. This paper describes the 5G field trial environment that was launched in Italy at the end of 2017. The aim is to evaluate the capability of the 5G network of supporting innovative services with reference to suitably designed key performance indicators and to evaluate the opportunities offered by these services. Indeed, vertical business use cases, specifically designed for the future 5G city, with a strong economic and social impact, are under implementation and will be evaluated. In particular, the paper provides a detailed description of the deployment of an actual complete integrated 5G network. It shows how 5G is effective enabling technology for a wide range of vertical business and use cases. Indeed, its flexibility allows to satisfy completely different performance requirements of real services. Some preliminary results, obtained during the first phase, are presented for a smart mobility scenario.

Description: LoRa (along with its upper layers definition—LoRaWAN) is one of the most promising Low Power Wide Area Network (LPWAN) technologies for implementing Internet of Things (IoT)-based applications. Although being a popular technology, several works in the literature have revealed vulnerabilities and risks regarding the security of LoRaWAN v1.0 (the official 1st specification draft). The LoRa-Alliance has built upon these findings and introduced several improvements in the security and architecture of LoRa. The result of these efforts resulted in LoRaWAN v1.1, released on 11 October 2017. This work aims at reviewing and clarifying the security aspects of LoRaWAN v1.1. By following ETSI guidelines, we provide a comprehensive Security Risk Analysis of the protocol and discuss several remedies to the security risks described. A threat catalog is presented, along with discussions and analysis in view of the scale, impact, and likelihood of each threat. To the best of the authors’ knowledge, this work is one of the first of its kind, by providing a detailed security risk analysis related to the latest version of LoRaWAN. Our analysis highlights important practical threats, such as end-device physical capture, rogue gateway and self-replay, which require particular attention by developers and organizations implementing LoRa networks.

Description: A Smart Home is characterized by the presence of a huge number of small, low power devices, along with more classical devices. According to the Internet of Things (IoT) paradigm, all of them are expected to be always connected to the Internet in order to provide enhanced services. In this scenario, an attacker can undermine both the network security and the user’s security/privacy. Traditional security measures are not sufficient, because they are too difficult to setup and are either too weak to effectively protect the user or too limiting for the new services effectiveness. The paper suggests to dynamically adapt the security level of the smart home network according to the user perceived risk level what we have called network sentiment analysis. The security level is not fixed, established by a central system (usually by the Internet Service Provider) but can be changed with the users cooperation. The security of the smart home network is improved by a distributed firewalls and Intrusion Detection Systems both to the smart home side as to the Internet Service Provider side. These two parts must cooperate and integrate their actions for reacting dynamically to new and on going threats. Moreover, the level of network sentiment detected can be propagate to nearby home networks (e.g., the smart home networks of the apartments inside a building) to increase/decrease their level of security, thus creating a true in-line Intrusion Prevention System (IPS). The paper also presents a test bed for Smart Home to detect and counteract to different attacks against the IoT sensors, Wi-Fi and Ethernet connections.

Description: To obtain the target webpages from many webpages, we proposed a Method for Filtering Pages by Similarity Degree based on Dynamic Programming (MFPSDDP). The method needs to use one of three same relationships proposed between two nodes, so we give the definition of the three same relationships. The biggest innovation of MFPSDDP is that it does not need to know the structures of webpages in advance. First, we address the design ideas with queue and double threads. Then, a dynamic programming algorithm for calculating the length of the longest common subsequence and a formula for calculating similarity are proposed. Further, for obtaining detailed information webpages from 200,000 webpages downloaded from the famous website “www.jd.com”, we choose the same relationship Completely Same Relationship (CSR) and set the similarity threshold to 0.2. The Recall Ratio (RR) of MFPSDDP is in the middle in the four filtering methods compared. When the number of webpages filtered is nearly 200,000, the PR of MFPSDDP is highest in the four filtering methods compared, which can reach 85.1%. The PR of MFPSDDP is 13.3 percentage points higher than the PR of a Method for Filtering Pages by Containing Strings (MFPCS).

Description: Research and development (R&D) are always oriented towards new discoveries, based on original terms or hypotheses, and their concluding outcomes are often uncertain. The present work focused on the degree of uncertainty for R&D activities. In fact, uncertainty makes it difficult to quantify the time and resources needed to achieve a final outcome, create a work plan and budget, and finalize the resulting “innovative” products or services that could be transferred or exchanged in a specific market. The present work attempts to indicate the degree of uncertainty of the research activities developed by a set of firms. The method used aimed to quantify the five criteria defined by the Manual of Frascati. Through the creation of an uncertainty cloud, a cone of uncertainty was defined following an approach based on project management. The evaluation grid was characterized by the decomposition of the different variables divided into quartiles, which allowed for the detection of the evolution of the project and each of its component. The ancillary objective aim was to also observe the development degree of these industries towards a framework of Industry 4.0.

Description: Conversational agents are reshaping our communication environment and have the potential to inform and persuade in new and effective ways. In this paper, we present the underlying technologies and the theoretical background behind a health-care platform dedicated to supporting medical stuff and individuals with movement disabilities and to providing advanced monitoring functionalities in hospital and home surroundings. The framework implements an intelligent combination of two research areas: (1) sensor- and camera-based monitoring to collect, analyse, and interpret people behaviour and (2) natural machine–human interaction through an apprehensive virtual assistant benefiting ailing patients. In addition, the framework serves as an important assistant to caregivers and clinical experts to obtain information about the patients in an intuitive manner. The proposed approach capitalises on latest breakthroughs in computer vision, sensor management, speech recognition, natural language processing, knowledge representation, dialogue management, semantic reasoning, and speech synthesis, combining medical expertise and patient history.

Description: Network Function Virtualization (NFV) has revolutionized the way network services are offered to end users. Individual network functions are decoupled from expensive and dedicated middleboxes and are now provided as software-based virtualized entities called Virtualized Network Functions (VNFs). NFV is often complemented with the Cloud Computing paradigm to provide networking functions to enterprise customers and end-users remote from their premises. NFV along with Cloud Computing has also started to be seen in Internet of Things (IoT) platforms as a means to provide networking functions to the IoT traffic. The intermix of IoT, NFV, and Cloud technologies, however, is still in its infancy creating a rich and open future research area. To this end, in this paper, we propose a novel approach to facilitate the placement and deployment of service chained VNFs in a network cloud infrastructure that can be extended using the Mobile Edge Computing (MEC) infrastructure for accommodating mission critical and delay sensitive traffic. Our aim is to minimize the end-to-end communication delay while keeping the overall deployment cost to minimum. Results reveal that the proposed approach can significantly reduce the delay experienced, while satisfying the Service Providers’ goal of low deployment costs.

Description: Network Function Virtualization is a new technology allowing for a elastic cloud and bandwidth resource allocation. The technology requires an orchestrator whose role is the service and resource orchestration. It receives service requests, each one characterized by a Service Function Chain, which is a set of service functions to be executed according to a given order. It implements an algorithm for deciding where both to allocate the cloud and bandwidth resources and to route the SFCs. In a traditional orchestration algorithm, the orchestrator has a detailed knowledge of the cloud and network infrastructures and that can lead to high computational complexity of the SFC Routing and Cloud and Bandwidth resource Allocation (SRCBA) algorithm. In this paper, we propose and evaluate the effectiveness of a scalable orchestration architecture inherited by the one proposed within the European Telecommunications Standards Institute (ETSI) and based on the functional separation of an NFV orchestrator in Resource Orchestrator (RO) and Network Service Orchestrator (NSO). Each cloud domain is equipped with an RO whose task is to provide a simple and abstract representation of the cloud infrastructure. These representations are notified of the NSO that can apply a simplified and less complex SRCBA algorithm. In addition, we show how the segment routing technology can help to simplify the SFC routing by means of an effective addressing of the service functions. The scalable orchestration solution has been investigated and compared to the one of a traditional orchestrator in some network scenarios and varying the number of cloud domains. We have verified that the execution time of the SRCBA algorithm can be drastically reduced without degrading the performance in terms of cloud and bandwidth resource costs.

Description: Texture evaluation is manually performed in general, and such analytical tasks can get cumbersome. In this regard, a neural network model is employed in this study. This paper describes a system that can estimate the food texture of snacks. The system comprises a simple equipment unit and an artificial neural network model. The equipment simultaneously examines the load and sound when a snack is pressed. The neural network model analyzes the load change and sound signals and then outputs a numerical value within the range (0,1) to express the level of textures such as “crunchiness” and “crispness”. Experimental results validate the model’s capacity to output moderate texture values of the snacks. In addition, we applied the convolutional neural network (CNN) model to classify snacks and the capability of the CNN model for texture estimation is discussed.

Description: Gamification, the use of game design elements in applications that are not games, has been developed to provide attractive environments and maintain user interest in several domains. In domains such as education, marketing and health, where gamification techniques are applied, user engagement in applications has increased. In these applications the protection of users’ privacy is an important aspect to consider, due to the applications obtaining a record of the personal information of their users. Thus, the purpose of this paper is to identify if applications where gamification is applied do respect users’ privacy. For the accomplishment of this aim, two main steps have been implemented. Since the main principle of gamification is the existence of game elements, the first step was to identify the set of game elements recorded in the literature that are commonly applied in various applications. Afterwards, an examination of the relationship between these elements and privacy requirements was implemented in order to identify which elements conflict with the privacy requirements leading to potential privacy violations and which elements do not. Α conceptual model according to the results of this examination was designed, which presents how elements conflict with requirements. Based on the results, there are indeed game elements which can lead to privacy violations. The results of this work provide valuable guidance to software developers, especially during the design stages of gamified applications since it helps them to consider the protection of users’ privacy in parallel from the early stages of the application development onwards.

Description: Most industrial and SCADA-like (supervisory control and data acquisition) systems use proprietary communication protocols, and hence interoperability is not fulfilled. However, the MODBUS TCP is an open de facto standard, and is used for some automation and telecontrol systems. It is based on a polling mechanism and follows the synchronous request–response pattern, as opposed to the asynchronous publish–subscribe pattern. In this study, polling-based and event-based protocols are investigated to realize an open and interoperable Industrial Internet of Things (IIoT) environment. Many Internet of Things (IoT) protocols are introduced and compared, and the message queuing telemetry transport (MQTT) is chosen as the event-based, publish–subscribe protocol. The study shows that MODBUS defines an optimized message structure in the application layer, which is dedicated to industrial applications. In addition, it shows that an event-oriented IoT protocol complements the MODBUS TCP but cannot replace it. Therefore, two scenarios are proposed to build the IIoT environment. The first scenario is to consider the MODBUS TCP as an IoT protocol, and build the environment using the MODBUS TCP on a standalone basis. The second scenario is to use MQTT in conjunction with the MODBUS TCP. The first scenario is efficient and complies with most industrial applications where the request–response pattern is needed only. If the publish–subscribe pattern is needed, the MQTT in the second scenario complements the MODBUS TCP and eliminates the need for a gateway; however, MQTT lacks interoperability. To maintain a homogeneous message structure for the entire environment, industrial data are organized using the structure of MODBUS messages, formatted in the UTF-8, and then transferred in the payload of an MQTT publish message. The open and interoperable environment can be used for Internet SCADA, Internet-based monitoring, and industrial control systems.

Description: The promising advancements in the telecommunications and automotive sectors over the years have empowered drivers with highly innovative communication and sensing capabilities, in turn paving the way for the next-generation connected and autonomous vehicles. Today, vehicles communicate wirelessly with other vehicles and vulnerable pedestrians in their immediate vicinity to share timely safety-critical information primarily for collision mitigation. Furthermore, vehicles connect with the traffic management entities via their supporting network infrastructure to become more aware of any potential hazards on the roads and for guidance pertinent to their current and anticipated speeds and travelling course to ensure more efficient traffic flows. Therefore, a secure and low-latency communication is highly indispensable in order to meet the stringent performance requirements of such safety-critical vehicular applications. However, the heterogeneity of diverse radio access technologies and inflexibility in their deployment results in network fragmentation and inefficient resource utilization, and these, therefore, act as bottlenecks in realizing the aims for a highly efficient vehicular networking architecture. In order to overcome such sorts of bottlenecks, this article brings forth the current state-of-the-art in the context of intelligent transportation systems (ITS) and subsequently proposes a software-defined heterogeneous vehicular networking (SDHVNet) architecture for ensuring a highly agile networking infrastructure to ensure rapid network innovation on-demand. Finally, a number of potential architectural challenges and their probable solutions are discussed.

Description: Information-centric networking integrates by design a pull-based model which brings in advantages in terms of control as well as of in-network caching strategies. Currently, ICN main areas of action concern content distribution and IoT, both of which are environments that often require support for periodic and even-triggered data transmission. Such environments can benefit from push-based communication to achieve faster data forwarding. This paper provides an overview on the current push-based mechanisms that can be applied to information-centric paradigms, explaining the trade-off associated with the different approaches. Moreover, the paper provides design guidelines for integrating push communications in information-centric networking, having as example the application of this networking architecture in IoT environments.

Description: The software defined networking (SDN) paradigm separates the control plane from the data plane, where an SDN controller receives requests from its connected switches and manages the operation of the switches under its control. Reassignments between switches and their controllers are performed dynamically, in order to balance the load over SDN controllers. In order to perform load balancing, most dynamic assignment solutions use a central element to gather information requests for reassignment of switches. Increasing the number of controllers causes a scalability problem, when one super controller is used for all controllers and gathers information from all switches. In a large network, the distances between the controllers is sometimes a constraint for assigning them switches. In this paper, a new approach is presented to solve the well-known load balancing problem in the SDN control plane. This approach implies less load on the central element and meeting the maximum distance constraint allowed between controllers. An architecture with two levels of load balancing is defined. At the top level, the main component called Super Controller, arranges the controllers in clusters, so that there is a balance between the loads of the clusters. At the bottom level, in each cluster there is a dedicated controller called Master Controller, which performs a reassignment of the switches in order to balance the loads between the controllers. We provide a two-phase algorithm, called Dynamic Controllers Clustering algorithm, for the top level of load balancing operation. The load balancing operation takes place at regular intervals. The length of the cycle in which the operation is performed can be shorter, since the top-level operation can run independently of the bottom level operation. Shortening cycle time allows for more accurate results of load balancing. Theoretical analysis demonstrates that our algorithm provides a near-optimal solution. Simulation results show that our dynamic clustering improves fixed clustering by a multiplicative factor of 5.

Description: The idea and perception of good cyber security protection remains at the forefront of many organizations’ information and communication technology strategy and investment. However, delving deeper into the details of its implementation reveals that organizations’ human capital cyber security knowledge bases are very low. In particular, the lack of social engineering awareness is a concern in the context of human cyber security risks. This study highlights pitfalls and ongoing issues that organizations encounter in the process of developing the human knowledge to protect from social engineering attacks. A detailed literature review is provided to support these arguments with analysis of contemporary approaches. The findings show that despite state-of-the-art cyber security preparations and trained personnel, hackers are still successful in their malicious acts of stealing sensitive information that is crucial to organizations. The factors influencing users’ proficiency in threat detection and mitigation have been identified as business environmental, social, political, constitutional, organizational, economical, and personal. Challenges with respect to both traditional and modern tools have been analyzed to suggest the need for profiling at-risk employees (including new hires) and developing training programs at each level of the hierarchy to ensure that the hackers do not succeed.

Description: In order to solve the problem of poor real-time performance, low accuracy and high computational complexity in the traditional process of locating and tracking of Direction of Arrival (DOA) of moving targets, this paper proposes a DOA algorithm based on the Direction Lock Loop (DILL) which adopts Lock Loop structure to realize the estimation and location of DOA and can adjust the direction automatically along with the changes of a signal’s angular variation to track the position of the signal. Meanwhile, to reduce the influence of nonlinearity and noise on its performance, the UKF filter is designed for eliminating interference of the estimated target signal to improve accuracy of the signal tracking and stability of the system. Simulation results prove that the algorithm can not only get a high resolution DOA estimate signal, but can also locate and track multiple mobile targets effectively with enhanced accuracy, efficiency and stability.

Description: Digital watermarking aims at protecting multimedia content by hiding into it some data that can be used for such purposes as, for example, intellectual property protection or integrity control. Sometimes, the owner of an image would prefer to not transmit, publish, or hand to a cloud service the original image, but rather an encrypted version of it. Encrypted images allow for less embedding room with respect to their unencrypted counterparts, as there is less spatial correlation to leverage upon. An architecture for embedding as payload the digital signature of an encrypted image, by means of a reversible watermarking technique, is presented in this work. A noteworthy point is the use of an identity-based cryptosystem to avoid certificate management and improve performance. In addition, the use of IBS-1, a recently proposed signature scheme, allows for a reduction in the size of the signature.

Description: In Internet of Things (IoT), the main driving technologies are considered to be tiny sensory objects. These objects cannot host traditional preventive and detective technologies to provide protection against the increasing threat sophistication. Furthermore, these solutions are limited to analyzing particular contextual information, for instance network information or files, and do not provide holistic context for risk analysis and response. Analyzing a part of a situation may lead to false alarms and later to unnecessary and incorrect configurations. To overcome these concerns, we proposed an event-driven adaptive security (EDAS) model for IoT. EDAS aims to observe security events (changes) generated by various things in the monitored IoT environment, investigates any intentional or unintentional risks associated with the events and adapts to it autonomously. It correlates different events in time and space to reduce any false alarms and provides a mechanism to predict attacks before they are realized. Risks are responded to autonomically by utilizing a runtime adaptation ontology. The mitigation action is chosen after assessing essential information, such as the risk faced, user preferences, device capabilities and service requirements. Thus, it selects an optimal mitigation action in a particular adverse situation. The objective of this paper is to investigate EDAS feasibility and its aptitude as a real-world prototype in a remote patient monitoring context. It details how EDAS can be a practical choice for IoT-eHealth in terms of the security, design and implementation features it offers as compared to traditional security controls. We have explained the prototype’s major components and have highlighted the key technical challenges.

Description: Existing Searchable Encryption (SE) solutions are able to handle simple Boolean search queries, such as single or multi-keyword queries, but cannot handle substring search queries over encrypted data that also involve identifying the position of the substring within the document. These types of queries are relevant in areas such as searching DNA data. In this paper, we propose a tree-based Substring Position Searchable Symmetric Encryption (SSP-SSE) to overcome the existing gap. Our solution efficiently finds occurrences of a given substring over encrypted cloud data. Specifically, our construction uses the position heap tree data structure and achieves asymptotic efficiency comparable to that of an unencrypted position heap tree. Our encryption takes O ( k n ) time, and the resulting ciphertext is of size O ( k n ) , where k is a security parameter and n is the size of stored data. The search takes O ( m 2 + o c c ) time and three rounds of communication, where m is the length of the queried substring and o c c is the number of occurrences of the substring in the document collection. We prove that the proposed scheme is secure against chosen-query attacks that involve an adaptive adversary. Finally, we extend SSP-SSE to the multi-user setting where an arbitrary group of cloud users can submit substring queries to search the encrypted data.

Description: The Windows Operating System (OS) is the most popular desktop OS in the world, as it has the majority market share of both servers and personal computing necessities. However, as its default signature-based security measures are ineffectual for detecting zero-day and stealth attacks, it needs an intelligent Host-based Intrusion Detection System (HIDS). Unfortunately, a comprehensive data set that reflects the modern Windows OS’s normal and attack surfaces is not publicly available. To fill this gap, in this paper two open data sets generated by the cyber security department of the Australian Defence Force Academy (ADFA) are introduced, namely: Australian Defence Force Academy Windows Data Set (ADFA-WD); and Australian Defence Force Academy Windows Data Set with a Stealth Attacks Addendum (ADFA-WD: SAA). Statistical analysis results based on these data sets show that, due to the low foot prints of modern attacks and high similarity of normal and attacked data, both these data sets are complex, and highly intelligent Host based Anomaly Detection Systems (HADS) design will be required.

Description: Mobile devices can be exploited for enabling people to interact with Internet of Things (IoT) services. The MicroApp Generator [1] is a service-composition tool for supporting the generation of mobile applications directly on the mobile device. The user interacts with the generated app by using the traditional touch-based interaction. This kind of interaction often is not suitable for elderly and special needs people that cannot see or touch the screen. In this paper, we extend the MicroApp Generator with an interaction approach enabling a user to interact with the generated app only by using his voice, which can be very useful to let special needs people live at home. To this aim, once the mobile app has been generated and executed, the system analyses and describes the user interface, listens to the user speech and performs the associated actions. A preliminary analysis has been conducted to assess the user experience of the proposed approach by a sample composed of elderly users by using a questionnaire as a research instrument.

Description: Energy efficiency is of major concern in wireless sensor networks, especially in difficult node deployment environments. Clustering is an effective method to save energy consumption and prolong the network lifetime. There are two kinds of clustering methods in hierarchical routing protocols, namely distributed control and centralized control. In the distributed strategy, the algorithm has good scalability, but it can easily lead to an uneven distribution of cluster heads. When the centralized strategy is used, the network is balanced and robust, but the overhead of clustering is large and the network latency is increased. In our proposed protocol, named DAHC, a mixture algorithm of distributed and centralized control is used to select a cluster head (CH). Simultaneously, we propose a novel approach to judge the cluster density and adjust according to the preset threshold. The simulation results show that our methods can evenly distribute the energy load on all nodes, save the energy consumption of communication and extend the network lifetime concurrently.

Description: We developed a mechanism for seamlessly providing weather data and long-term historical climate data from a gridded data source through an international standard web API, which was the Sensor Observation Service (SOS) defined by the Open Geospatial Consortium (OGC). The National Agriculture and Food Research Organization (NARO) Japan has been providing gridded climate data consisting of nine daily meteorological variables, which are average, minimum, maximum of air temperature, relative humidity, sunshine duration, solar radiant exposure, downward longwave radiation, precipitation and wind speed for 35 years covering Japan. The gridded data structure is quite useful for spatial analysis, such as developing crop suitability maps and monitoring regional crop development. Individual farmers, however, make decisions using historical climate information and forecasts for an incoming cropping season of their farms. In this regard, climate data at a point-based structure are convenient for application development to support farmers’ decisions. Through the proposed mechanism in this paper, the agricultural applications and analysis can request point-based climate data from a gridded data source through the standard API with no need to deal with the complicated hierarchical data structure of the gridded climate data source. Clients can easily obtain data and metadata by only accessing the service endpoint. The mechanism also provides several web bindings and data encodings for the clients’ convenience. Caching, including the pre-caching mechanism, was developed and evaluated to secure an effective response time. The mechanism enhances the accessibility and usability of the gridded weather data source, as well as SOS API for agricultural applications.

Description: Social and mobile computing open up new possibilities for integrating citizens’ information, knowledge, and social capital in emergency management (EM). This participation can improve the capacity of local agencies to respond to unexpected events by involving citizens not only as first line informants, but also as first responders. This participation could contribute to build resilient communities aware of the risks they are threatened by and able to mobilize their social capital to cope with them and, in turn, decrease the impact of threats and hazards. However for this participation to be possible organizations in charge of EM need to realize that involving citizens does not interfere with their protocols and that citizens are a valuable asset that can contribute to the EM process with specific skills and capabilities. In this paper we discuss the design challenges of using social and mobile computing to move to a more participatory EM process that starts by empowering both citizens and organizations in a coproduction service envisioned as a partnership effort. As an example, we describe a case study of a participatory design approach that involved professional EM workers and decision makers in an effort to understand the challenges of using technology-based solutions to integrate citizen skills and capabilities in their operation protocols. The case study made it possible to identify specific roles that citizens might play in a crisis or disaster and to envision scenarios were technologies could be used to integrate their skills into the EM process. In this way the paper contributes to the roles and the scenarios of theory-building about coproduction in EM services.

Description: Trust is one of the most important factors that influence the successful application of network service environments, such as e-commerce, wireless sensor networks, and online social networks. Computation models associated with trust and reputation have been paid special attention in both computer societies and service science in recent years. In this paper, a dynamical computation model of reputation for B2C e-commerce is proposed. Firstly, conceptions associated with trust and reputation are introduced, and the mathematical formula of trust for B2C e-commerce is given. Then a dynamical computation model of reputation is further proposed based on the conception of trust and the relationship between trust and reputation. In the proposed model, classical varying processes of reputation of B2C e-commerce are discussed. Furthermore, the iterative trust and reputation computation models are formulated via a set of difference equations based on the closed-loop feedback mechanism. Finally, a group of numerical simulation experiments are performed to illustrate the proposed model of trust and reputation. Experimental results show that the proposed model is effective in simulating the dynamical processes of trust and reputation for B2C e-commerce.

Description: Everyone likes Porsche but few can afford it. Budget constraints always play a critical role in a customer’s decision-making. The literature disproportionally focuses on how firms can induce customer valuations toward the product, but does not address how to assess the influence of budget constraints. We study these questions in the context of a referral reward program (RRP). RRP is a prominent marketing strategy that utilizes recommendations passed from existing customers to their friends and effectively stimulates word of mouth (WoM). We build a stylized game-theoretical model with a nested Stackelberg game involving three players: a firm, an existing customer, and a potential customer who is a friend of the existing customer. The budget is the friend’s private information. We show that RRPs might be optimal when the friend has either a low or a high valuation, but they work differently in each situation because of the budget. Furthermore, there are two budget thresholds, a fixed one and a variable one, which limit a firm’s ability to use rewards.

Description: The appeal of e-Government users to retain control over their personal information, while making use of advanced governmental electronic services through interconnected and interoperable deployments, can be assisted by the incorporation of privacy policy and Preferences documents. This paper addresses the formulation of light-weight and accurate privacy policies, while preserving compliance with underlying legal and regulatory framework. Through the exploitation of existing governmental hierarchies, a multitier approach is proposed able to support diverge data needs and processing requests imposed by service providers. The incorporation of this approach into e-Government environments will reduce the administrative workload, imposed by the inclusion of privacy policy documents, and promote the implementation and provision of user-centric and data privacy aware electronic services.

Description: Emerging technologies such as Software-Defined Networks (SDN) and Network Function Virtualization (NFV) promise to address cost reduction and flexibility in network operation while enabling innovative network service delivery models. However, operational network service delivery solutions still need to be developed that actually exploit these technologies, especially at the multi-provider level. Indeed, the implementation of network functions as software running over a virtualized infrastructure and provisioned on a service basis let one envisage an ecosystem of network services that are dynamically and flexibly assembled by orchestrating Virtual Network Functions even across different provider domains, thereby coping with changeable user and service requirements and context conditions. In this paper we propose an approach that adopts Service-Oriented Architecture (SOA) technology-agnostic architectural guidelines in the design of a solution for orchestrating and dynamically chaining Virtual Network Functions. We discuss how SOA, NFV, and SDN may complement each other in realizing dynamic network function chaining through service composition specification, service selection, service delivery, and placement tasks. Then, we describe the architecture of a SOA-inspired NFV orchestrator, which leverages SDN-based network control capabilities to address an effective delivery of elastic chains of Virtual Network Functions. Preliminary results of prototype implementation and testing activities are also presented. The benefits for Network Service Providers are also described that derive from the adaptive network service provisioning in a multi-provider environment through the orchestration of computing and networking services to provide end users with an enhanced service experience.

Description: The aim of the study was deepening the knowledge of livestock innovations knowledge on small-scale farms in developing countries. First, we developed a methodology focused on identifying potential appropriate livestock innovations for smallholders and grouped them in innovation areas, defined as a set of well-organized practices with a business purpose. Finally, a process management program (PMP) was evaluated according to the livestock innovation level and viability of the small-scale farms. Logistic regression was used to evaluate the impact of PMP on the economic viability of the farm. Information from 1650 small-scale livestock farms in Mexico was collected and the innovations were grouped in five innovation areas: A1. Management, A2. Feeding, A3. Genetic, A4. Reproduction and A5. Animal Health. The resulting innovation level in the system was low at 45.7% and heterogeneous among areas. This study shows the usefulness of the methodology described and confirms that implementing a PMP allows improving the viability an additional 21%, due to a better integration of processes, resulting in more efficient management.

Description: Since the turn of the 21st century, we have seen a surge of studies on the state of U.S. education addressing issues such as cost, graduation rates, retention, achievement, engagement, and curricular outcomes. There is an expectation that graduates should be able to enter the workplace equipped to take on complex and “messy” or ill-structured problems as part of their professional and everyday life. In the context of online learning, we have identified two key issues that are elusive (hard to capture and make visible): learning with ill-structured problems and the interaction of social and individual learning. We believe that the intersection between learning and analytics has the potential, in the long-term, to minimize the elusiveness of deep learning. A proposed analytics model is described in this article that is meant to capture and also support further development of a learner’s reflective sensemaking.

Description: In this paper, an attempt is made to analyze the potential threats and consequences of cyber conflicts and, in particular, the risks of a global cyber conflict. The material is based on a comprehensive analysis of the nature of cyber conflict and its elements from both technical and societal points of view. The approach used in the paper considers the societal component as an essential part of cyber conflicts, allowing basics of cyber conflicts often disregarded by researchers and the public to be highlighted. Finally, the conclusion offers an opportunity to consider cyber conflict as the most advanced form of modern warfare, which imposes the most serious threat and whose effect could be comparable to weapons of mass destruction.

Description: What is the boundary between a vigorous argument and a breakdown of relations? What drives a group of individuals across it? Taking Wikipedia as a test case, we use a hidden Markov model to approximate the computational structure and social grammar of more than a decade of cooperation and conflict among its editors. Across a wide range of pages, we discover a bursty war/peace structure where the systems can become trapped, sometimes for months, in a computational subspace associated with significantly higher levels of conflict-tracking “revert” actions. Distinct patterns of behavior characterize the lower-conflict subspace, including tit-for-tat reversion. While a fraction of the transitions between these subspaces are associated with top-down actions taken by administrators, the effects are weak. Surprisingly, we find no statistical signal that transitions are associated with the appearance of particularly anti-social users, and only weak association with significant news events outside the system. These findings are consistent with transitions being driven by decentralized processes with no clear locus of control. Models of belief revision in the presence of a common resource for information-sharing predict the existence of two distinct phases: a disordered high-conflict phase, and a frozen phase with spontaneously-broken symmetry. The bistability we observe empirically may be a consequence of editor turn-over, which drives the system to a critical point between them.

Description: In recent years, train passengers have been transferring increasing amounts of data using mobile devices. Wireless networks with mobile relay nodes support broadband wireless communications for passengers of such vehicles using backhaul links. However, the mobility management entity reuses the handover of existing user equipment, resulting in the handover of the Long-Term Evolution network being unsuitable for user equipment within the cabins of vehicles traveling at high speed. In this paper, we propose a self-optimizing handover hysteresis scheme with dual mobile relay nodes for wireless networks in high-speed mobile environments. The proposed mechanism tunes the hysteresis and cell individual offset handover parameters based on the velocity of the vehicle and the handover performance indicator, which affects the handover triggering decision and performance. The results of simulations conducted in which the performance of the proposed scheme was compared to that of an existing scheme show that the proposed scheme can reduce the number of radio link failures and service interruptions during handover procedures.

Description: We describe how statistical predictive models might play an expanded role in educational analytics by giving students automated, real-time information about what their current performance means for eventual success in eLearning environments. We discuss how an online messaging system might tailor information to individual students using predictive analytics. The proposed system would be data-driven and quantitative; e.g., a message might furnish the probability that a student will successfully complete the certificate requirements of a massive open online course. Repeated messages would prod underperforming students and alert instructors to those in need of intervention. Administrators responsible for accreditation or outcomes assessment would have ready documentation of learning outcomes and actions taken to address unsatisfactory student performance. The article’s brief introduction to statistical predictive models sets the stage for a description of the messaging system. Resources and methods needed to develop and implement the system are discussed.

Description: Background: In 2011, the Veterans Health Administration (VHA) consulted with the Project ECHO (Extension for Community Healthcare Outcomes) team at the University of New Mexico, Albuquerque, to reproduce their successful model within the VHA. Methods: The VHA launched SCAN-ECHO (Specialty Care Access Network-Extension for Community Healthcare Outcomes), a multisite videoconferencing system to conduct live clinical consultations between specialists at a VHA Medical Center (hospital) and primary care providers stationed at satellite VHA CBOCs (Community-Based Outpatient Clinic). Results: Analysis of the first three years rendered a mean attendee satisfaction of 89.53% and a consultation satisfaction score of 88.10%. About half of the SCAN-ECHO consultations resulted in patients receiving their treatment from their local primary care providers; the remaining half were referred to the VHA Medical Center when the treatment involved equipment or services not available at the CBOCs (e.g., MRI, surgery). Conclusion: This paper details the setup, operation logistics and preliminary findings, suggesting that SCAN-ECHO is a viable model for providing quality specialty clinical consultation service, prompter access to care, reduced commutes and continuing education. Additionally, the use of a secured Internet-based videoconferencing system that supports connectivity to multiple (mobile) devices could expand the utilization of this service.

Description: The significance of approaches to technology-enhanced learning (TEL) has increased rapidly during the last few years. Nowadays in education different approaches such as game-based learning, web-based learning, video-based online courses, and many others are used on a daily basis. However, what defines the success of technology-enhanced learning approaches and how can such approaches be developed in a structured way? Furthermore, what different aspects have to be considered while doing so? To answer these questions, 4567 publications were analyzed in this present literature review in order to recognize the different aspects of implementing technology-enhanced learning approaches. Finally, 20 categories are defined in four main areas that summarize all the aspects in the field of technology-enhanced learning. Moreover, the study also reveals research areas that are important but hardly investigated in the observed journals of this study.

Description: Automated food and drink recognition methods connect to cloud-based lookup databases (e.g., food item barcodes, previously identified food images, or previously classified NIR (Near Infrared) spectra of food and drink items databases) to match and identify a scanned food or drink item, and report the results back to the user. However, these methods remain of limited value if we cannot further reason with the identified food and drink items, ingredients and quantities/portion sizes in a proposed meal in various contexts; i.e., understand from a semantic perspective their types, properties, and interrelationships in the context of a given user’s health condition and preferences. In this paper, we review a number of “food ontologies”, such as the Food Products Ontology/FOODpedia (by Kolchin and Zamula), Open Food Facts (by Gigandet et al.), FoodWiki (Ontology-driven Mobile Safe Food Consumption System by Celik), FOODS-Diabetes Edition (A Food-Oriented Ontology-Driven System by Snae Namahoot and Bruckner), and AGROVOC multilingual agricultural thesaurus (by the UN Food and Agriculture Organization—FAO). These food ontologies, with appropriate modifications (or as a basis, to be added to and further expanded) and together with other relevant non-food ontologies (e.g., about diet-sensitive disease conditions), can supplement the aforementioned lookup databases to enable progression from the mere automated identification of food and drinks in our meals to a more useful application whereby we can automatically reason with the identified food and drink items and their details (quantities and ingredients/bromatological composition) in order to better assist users in making the correct, healthy food and drink choices for their particular health condition, age, body weight/BMI (Body Mass Index), lifestyle and preferences, etc.

Description: In this paper, we address the problem of energy conservation and optimization in residential environments by providing users with useful information to solicit a change in consumption behavior. Taking care to highly limit the costs of installation and management, our work proposes a Non-Intrusive Load Monitoring (NILM) approach, which consists of disaggregating the whole-house power consumption into the individual portions associated to each device. State of the art NILM algorithms need monitoring data sampled at high frequency, thus requiring high costs for data collection and management. In this paper, we propose an NILM approach that relaxes the requirements on monitoring data since it uses total active power measurements gathered at low frequency (about 1 Hz). The proposed approach is based on the use of Factorial Hidden Markov Models (FHMM) in conjunction with context information related to the user presence in the house and the hourly utilization of appliances. Through a set of tests, we investigated how the use of these additional context-awareness features could improve disaggregation results with respect to the basic FHMM algorithm. The tests have been performed by using Tracebase, an open dataset made of data gathered from real home environments.

Description: The editors of Future Internet would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2015. [...]

Description: Most of the existing research works on the intrusion detection problem in a wireless sensor network (WSN) assume linear or random mobility patterns in abstracting intruders’ models in traversing the WSN field. However, in real-life WSN applications, an intruder is usually an intelligent mobile robot with environment learning and detection avoidance capability (i.e., the capability to avoid surrounding sensors). Due to this, the literature results based on the linear or random mobility models may not be applied to the real-life WSN design and deployment for efficient and effective intrusion detection in practice. This motivates us to investigate the impact of intruder’s intelligence on the intrusion detection problem in a WSN for various applications. To be specific, we propose two intrusion algorithms, the pinball and flood-fill algorithms, to mimic the intelligent motion and behaviors of a mobile intruder in detecting and circumventing nearby sensors for detection avoidance while heading for its destination. The two proposed algorithms are integrated into a WSN framework for intrusion detection analysis in various circumstances. Monte Carlo simulations are conducted, and the results indicate that: (1) the performance of a WSN drastically changes as a result of the intruder’s intelligence in avoiding sensor detections and intrusion algorithms; (2) network parameters, including node density, sensing range and communication range, play a crucial part in the effectiveness of the intruder’s intrusion algorithms; and (3) it is imperative to integrate intruder’s intelligence in the WSN research for intruder detection problems under various application circumstances.

Description: Current-generation assessment tools used in K-12 and post-secondary education are limited in the type of questions they support; this limitation makes it difficult for instructors to navigate their assessment engines. Furthermore, the question types tend to score low on Bloom’s Taxonomy. Dedicated learning management systems (LMS) such as Blackboard, Moodle and Canvas are somewhat better than informal tools as they offer more question types and some randomization. Still, question types in all the major LMS assessment engines are limited. Additionally, LMSs place a heavy burden on teachers to generate online assessments. In this study we analyzed the top three LMS providers to identify inefficiencies. These inefficiencies in LMS design, point us to ways to ask better questions. Our findings show that teachers have not adopted current tools because they do not offer definitive improvements in productivity. Therefore, we developed LiquiZ, a design for a next-generation assessment engine that reduces user effort and provides more advanced question types that allow teachers to ask questions that can currently only be asked in one-on-one demonstration. The initial LiquiZ project is targeted toward STEM subjects, so the question types are particularly advantageous in math or science subjects.

Description: Meteorological technology has evolved rapidly in recent years to provide enormous, accurate and personalized advantages in the public service. Large volumes of observational data are generated gradually by technologies such as geographical remote sensing, meteorological radar satellite, etc. that makes data analysis in weather forecasting more precise but also poses a threat to the traditional method of data storage. In this paper, we present MHBase, (Meteorological data based on HBase (Hadoop Database), a distributed real-time query scheme for meteorological data based on HBase. The calibrated data obtained from terminal devices will be partitioned into HBase and persisted to HDFS (the Hadoop Distributed File System). We propose two algorithms (the Indexed Store and the Indexed Retrieve Algorithms) to implement a secondary index using HBase Coprocessors, which allow MHbase to provide high performance data querying on columns other than rowkey. Experimental results show that the performance of MHBase can satisfy the basic demands of meteorological business services.

Description: The Public Key Infrastructure (PKI) is crumbling, partially due to the lack of a strong understanding of how encryption actually works, but also due to weaknesses in its implementation. This paper outlines an Internet storage technique using secret sharing methods which could be used to overcome the problems inherent with PKI, while supporting new types of architectures incorporating such things as automated failover and break-glass data recovery. The paper outlines a novel architecture: SECRET, which supports a robust cloud-based infrastructure with in-built privacy and failover. In order to understand the performance overhead of SECRET, the paper outlines a range of experiments that investigate the overhead of this and other secret share methods.

Description: This is a study of the way in which YouTubers’ media metrics influence the effect of their one-sided messages (1SMs) and two-sided messages (2SMs), providing theoretical explanations based on the elaboration likelihood model. Its main objective is the proposition and testing of: (i) the interaction effect between type of message and media metrics of the YouTuber on customers’ responses, and (ii) the moderation of individuals’ conformity intention for the interaction effect between type of message and media metrics on customers’ responses. The results of an experiment showed that high YouTubers’ media metrics have more effect for 1SMs and less effect for 2SMs. Additionally, conformity intention moderates the effect of the interaction type of message X media metrics. A high level of conformity intention neutralizes the interaction effect between YouTubers’ media metrics and message sidedness. This study makes a theoretical contribution to research into online content and information use, providing explanations of how media metrics of a vlog influence the effect of two types of messages.