Miracle-dz/UniFAR-FTUs huggingface dataset:
SAREF4health: Towards IoT standard-based ontology-driven cardiac e-health systems

Recently, a number of ontology-driven healthcare systems have been leveraged by the Internet-of-Things (IoT) technologies that offer opportunities to improve abnormal situation detection when integrating medical wearables and cloud infrastructure. Usually, these systems rely on standardised IoT ontologies to represent sensor data observations. The ETSI Smart Applications REFerence ontology (SAREF) is an extensible industry-oriented standard. In this paper, we explain the need for interoperability of IoT healthcare applications and the role of standardised ontologies to achieve semantic interoperability. In particular, we discuss the verbosity problem of SAREF when used for real-time electrocardiography (ECG), emphasizing the requirement of representing time series. We compared the main ontologies in this context, according to quality, message size (payload), IoT-orientation and standardisation. Here we describe the ﬁrst attempt to extend SAREF for speciﬁc e-Health use cases related to ECG data, the SAREF4health extension, which tackles the verbosity problem. Ontology-driven conceptual modelling was applied to develop SAREF4health, in which an ECG ontology grounded in the Uniﬁed Foundational Ontology (UFO), which plays the role of a reference model. The methodology was enhanced by following a standardisation procedure and considering the RDF implementation of the HL7 Fast Healthcare Interoperability Resources (FHIR) standard. The validation of SAREF4health includes the responses to competency questions, as well as the development and tests of an IoT Early Warning System prototype that uses ECG data and collision identiﬁcation to detect accidents with truck drivers in a port area. This prototype integrates an existing ECG wearable with a cloud infrastructure, demonstrating the performance impact of SAREF4health considering IoT constraints. Our results show that SAREF4health enables the semantic interoperability of IoT solutions that need to deal with frequency-based time series. Design decisions regarding the trade-off between ontology quality and aggregation representation are also discussed.
A Fast and Noise Tolerable Binarization Method for Automatic License Plate Recognition in the Open Environment in Taiwan

License plate recognition is widely used in our daily life. Image binarization, which is a process to convert an image to white and black, is an important step of license plate recognition. Among the proposed binarization methods, Otsu method is the most famous and commonly used one in a license plate recognition system since it is the fastest and can reach a comparable recognition accuracy. The main disadvantage of Otsu method is that it is sensitive to luminance effect and noise, and this property is impractical since most vehicle images are captured in an open environment. In this paper, we propose a system to improve the performance of automatic license plates reorganization in the open environment in Taiwan. Our system uses a binarization method which is inspired by the symmetry principles. Experimental results showed that when our method has a similar time complexity to that of Otsu, our method can improve the recognition rate up to 1.30 times better than Otsu.
The impact of language on retweeting during acute natural disasters: uncertainty reduction and language expectancy perspectives

PurposeSocial media have increasingly gained credibility as information sources in emergencies. Retweeting or resharing nature has made Twitter a popular medium of information dissemination. The purpose of this article is to enhance our understanding of both linguistic style and content properties (i.e. both affective and informational contents) that drives resharing behavior or virality of disaster messages on Twitter. We investigate this issue in the context of natural disaster crisis.Design/methodology/approachIn this study, the authors develop, drawing upon language expectancy and uncertainty reduction theories as an enabling framework, hypotheses about how the language (i.e. style and content) influence resharing behavior. They employ a natural language processing of disaster tweets to examine how the language – linguistic style (concrete and interactive language) and linguistic content (information- and affect-focused language) – affects resharing behavior on Twitter during natural disasters. To examine the effects of both linguistic style and content factors on virality, a series of negative binomial regressions were conducted, particularly owing to the highly skewed count data.FindingsOur analysis of tweets from the 2013 Colorado floods shows that resharing disasters tweets increases with the use of concrete language style during acute emergencies. Interactive language is also positively associated with retweet frequency. In addition, neither positive nor negative emotional tweets drive down resharing during acute crises, while information-focused language content has a significantly positive effect on virality.Practical implicationsAgencies for public safety and disaster management or volunteer organizations involved in disseminating crisis and risk information to the public may leverage the impacts of the linguistic style and language content through the lens of our research model. The findings encourage practitioners to focus on the role of linguistic style cues during acute disasters. Specifically, from the uncertainty reduction perspective, using concrete language in the disaster tweets is the expected norm, leading to a higher likelihood of virality. Also, interactively frame disaster tweets are more likely to be diffused to a larger number of people on Twitter.Originality/valueThe language that people use offer important psychological cue to their intentions and motivations. However, the role of language on Twitter has largely been ignored in this crisis communication and few prior studies have examined the relationship between language and virality during acute emergencies. This article explains the complex and multifaceted nature of information resharing behavior using a multi-theoretical approach – including uncertainty reduction and language expectancy theory – to understand effects of language style and content cues on resharing behavior in the context of natural crisis events.
Orienting supersingular isogeny graphs

Abstract We introduce a category of 𝓞-oriented supersingular elliptic curves and derive properties of the associated oriented and nonoriented ℓ-isogeny supersingular isogeny graphs. As an application we introduce an oriented supersingular isogeny Diffie-Hellman protocol (OSIDH), analogous to the supersingular isogeny Diffie-Hellman (SIDH) protocol and generalizing the commutative supersingular isogeny Diffie-Hellman (CSIDH) protocol.
EDR: A generic approach for the distribution of rule-based reasoning in a Cloud-Fog continuum

The successful deployment of the Semantic Web of Things (SWoT) requires the adaptation of the Semantic Web principles and technologies to the constraints of the IoT domain, which is the challenging research direction we address here. In this context we promote distributed reasoning approaches in IoT systems by implementing a hybrid deployment of reasoning rules relying on the complementarity of Cloud and Fog computing. Our solution benefits from the complementarity between Cloud and Fog infrastructures. Indeed, remote powerful Cloud computation resources are essential to the deployment of scalable IoT applications, and locally distributed constrained Fog resources, close to data producers, enable low-latency decision making. Moreover, as IoT networks are open and evolutive, the computation should be dynamically distributed across Fog nodes according to the transformation of the network topology. For this purpose, we propose the Emergent Distributed Reasoning (EDR) approach, implementing a dynamic distributed deployment of reasoning rules in a Cloud-Fog IoT architecture. We elaborated mechanisms enabling the genericity and the dynamicity of EDR. We evaluated its scalability and applicability in a simulated smart factory use-case. The complementarity between Fog and Cloud in this context is assessed based on the experimentation conducted.
Do Products Respond to User Desires? A Case Study. Errors and Successes in the Design Process, under the Umbrella of Emotional Design

This article introduces a methodological approach to the evaluation of different industrial products according to Norman’s approach and dimensions, focusing on a specific case study. The study also shows different possibilities to guide industrial designers during the design process in order to create products with high emotional value. For those, the case study was done with 330 target specific users, submitting nine prototypes (designed for different targets) to the user experience evaluation and product perception analysis. The evaluated proposals were selected from a total of 45. The results show the visceral, behavioural and reflective levels perceived by those users to whom each product is intended, as well as the target deviation within the design process. In this sense, the emotional response reveals the asymmetric character of perception according to Norman’s dimensions.
Increase in Absolute Leaf Water Content Tends to Keep Pace with That of Leaf Dry Mass - Evidence from Bamboo Plants

Leaves, as the most important photosynthetic organ of plants, are intimately associated with plant function and adaptation to environmental changes. The scaling relationship of the leaf dry mass (or the fresh mass) vs. leaf surface area has been referred to as “diminishing returns”, suggesting that the leaf area fails to increase in proportion to leaf dry mass (or fresh mass). However, previous studies used materials across different families, and there is lack of studies testing whether leaf fresh mass is proportional to the leaf dry mass for the species in the same family, and examining the influence of the scaling of leaf dry mass vs. fresh mass on two kinds of diminishing returns based on leaf dry mass and fresh mass. Bamboo plants (Poaceae: Bambusoideae) are good materials for doing such a study, which have astonishingly similar leaf shapes across species. Bamboo leaves have a typical parallel venation pattern. In general, a parallel venation pattern tends to produce a more stable symmetrical leaf shape than the pinnate and palmate venation patterns. The symmetrical parallel veins enable leaves to more regularly hold water, which is more likely to result in a proportional relationship between the leaf dry mass and absolute water content, which consequently determines whether the scaling exponent of the leaf dry mass vs. area is significantly different from (or the same as) that of the leaf fresh mass vs. area. In the present study, we used the data of 101 bamboo species, cultivars, forms and varieties (referred to as 101 (bamboo) taxa below for convenience) to analyze the scaling relationships between the leaf dry mass and area, and between leaf fresh mass and area. We found that the confidence intervals of the scaling exponents of the leaf fresh mass vs. dry mass of 68 out of the 101 taxa included unity, which indicates that for most bamboo species (67.3%), the increase in leaf water mass keeps pace with that of leaf dry mass. There was a significant scaling relationship between either leaf dry mass or fresh mass, and the leaf surface area for each studied species. We found that there was no significant difference between the scaling exponent of the leaf dry mass vs. leaf area and that of the leaf fresh mass vs. leaf area when the leaf dry mass was proportional to the leaf fresh mass. The goodness of fit to the linearized scaling relationship of the leaf fresh mass vs. area was better than that of the leaf dry mass vs. area for each of the 101 bamboo taxa. In addition, there were significant differences in the normalized constants of the leaf dry mass vs. fresh mass among the taxa (i.e., the differences in leaf water content), which implies the difference in the adaptabilities to different environments across the taxa.
Mobile Money Fraud Prediction - A Cross-Case Analysis on the Efficiency of Support Vector Machines, Gradient Boosted Decision Trees, and Naïve Bayes Algorithms

The onset of COVID-19 has re-emphasized the importance of FinTech especially in developing countries as the major powers of the world are already enjoying the advantages that come with the adoption of FinTech. Handling of physical cash has been established as a means of transmitting the novel corona virus. Again, research has established that, been unbanked raises the potential of sinking one into abject poverty. Over the years, developing countries have been piloting the various forms of FinTech, but the very one that has come to stay is the Mobile Money Transactions (MMT). As mobile money transactions attempt to gain a foothold, it faces several problems, the most important of them is mobile money fraud. This paper seeks to provide a solution to this problem by looking at machine learning algorithms based on support vector machines (kernel-based), gradient boosted decision tree (tree-based) and Naïve Bayes (probabilistic based) algorithms, taking into consideration the imbalanced nature of the dataset. Our experiments showed that the use of gradient boosted decision tree holds a great potential in combating the problem of mobile money fraud as it was able to produce near perfect results.
Data Lake Governance: Towards a Systemic and Natural Ecosystem Analogy

The realm of big data has brought new venues for knowledge acquisition, but also major challenges including data interoperability and effective management. The great volume of miscellaneous data renders the generation of new knowledge a complex data analysis process. Presently, big data technologies provide multiple solutions and tools towards the semantic analysis of heterogeneous data, including their accessibility and reusability. However, in addition to learning from data, we are faced with the issue of data storage and management in a cost-effective and reliable manner. This is the core topic of this paper. A data lake, inspired by the natural lake, is a centralized data repository that stores all kinds of data in any format and structure. This allows any type of data to be ingested into the data lake without any restriction or normalization. This could lead to a critical problem known as data swamp, which can contain invalid or incoherent data that adds no values for further knowledge acquisition. To deal with the potential avalanche of data, some legislation is required to turn such heterogeneous datasets into manageable data. In this article, we address this problem and propose some solutions concerning innovative methods, derived from a multidisciplinary science perspective to manage data lake. The proposed methods imitate the supply chain management and natural lake principles with an emphasis on the importance of the data life cycle, to implement responsible data governance for the data lake.
Skew Generalized Normal Innovations for the AR(p) Process Endorsing Asymmetry

The assumption of symmetry is often incorrect in real-life statistical modeling due to asymmetric behavior in the data. This implies a departure from the well-known assumption of normality defined for innovations in time series processes. In this paper, the autoregressive (AR) process of order p (i.e., the AR(p) process) is of particular interest using the skew generalized normal (SGN) distribution for the innovations, referred to hereafter as the ARSGN(p) process, to accommodate asymmetric behavior. This behavior presents itself by investigating some properties of the SGN distribution, which is a fundamental element for AR modeling of real data that exhibits non-normal behavior. Simulation studies illustrate the asymmetry and statistical properties of the conditional maximum likelihood (ML) parameters for the ARSGN(p) model. It is concluded that the ARSGN(p) model accounts well for time series processes exhibiting asymmetry, kurtosis, and heavy tails. Real time series datasets are analyzed, and the results of the ARSGN(p) model are compared to previously proposed models. The findings here state the effectiveness and viability of relaxing the normal assumption and the value added for considering the candidacy of the SGN for AR time series processes.
Reduced Complexity Detection of Narrowband Secondary Synchronization Signal for NB-IoT Communication Systems

Narrowband Internet of Things is one of the most promising technologies to support low cost, massive connection, deep coverage, and low power consumption. In this paper, a computationally efficient narrowband secondary synchronization signal detection method is proposed in the narrowband Internet of Things system. By decoupling the detection of complementary sequence and Zadoff–Chu sequence that make up the synchronization signal sequence, the search space of narrowband secondary synchronization signal hypotheses is reduced. Such a design strategy along with the use of the symmetric property of synchronization signals allows reduced-complexity synchronization signal detection in the narrowband Internet of Things system. Both theoretical and simulation results are provided to verify the usefulness of the proposed detector. It is shown via simulation results that the complexity of the proposed detection method is significantly reduced while producing some performance degradation, compared to the conventional detection method.
Energy and Delay Efficient Computation Offloading Solutions for Edge Computing

This thesis collects a selective set of outcomes of a PhD course in Electronics, Telecommunications, and Information Technologies Engineering and it is focused on designing techniques to optimize computational resources in different wireless communication environments. Mobile Edge Computing (MEC) is a novel and distributed computational paradigm that has emerged to address the high users demand in 5G. In MEC, edge devices can share their resources to collaborate in terms of storage and computation. One of the computational sharing techniques is computation offloading, which brings a lot of advantages to the network edge, from lower communication, to lower energy consumption for computation. However, the communication among the devices should be managed such that the resources are exploited efficiently. To this aim, in this dissertation, computation offloading in different wireless environments with different number of users, network traffic, resource availability and devices' location are analyzed in order to optimize the resource allocation at the network edge. To better organize the dissertation, the studies are classified in four main sections. In the first section, an introduction on computational sharing technologies is given. Later, the problem of computation offloading is defined, and the challenges are introduced. In the second section, two partial offloading techniques are proposed. While in the first one, centralized and distributed architectures are proposed, in the second work, an Evolutionary Algorithm for task offloading is proposed. In the third section, the offloading problem is seen from a different perspective where the end users can harvest energy from either renewable sources of energy or through Wireless Power Transfer. In the fourth section, the MEC in vehicular environments is studied. In one work a heuristic is introduced in order to perform the computation offloading in Internet of Vehicles and in the other a learning-based approach based on bandit theory is proposed.
Polyp Segmentation with Fully Convolutional Deep Neural Networks—Extended Evaluation Study

Analysis of colonoscopy images plays a significant role in early detection of colorectal cancer. Automated tissue segmentation can be useful for two of the most relevant clinical target applications—lesion detection and classification, thereby providing important means to make both processes more accurate and robust. To automate video colonoscopy analysis, computer vision and machine learning methods have been utilized and shown to enhance polyp detectability and segmentation objectivity. This paper describes a polyp segmentation algorithm, developed based on fully convolutional network models, that was originally developed for the Endoscopic Vision Gastrointestinal Image Analysis (GIANA) polyp segmentation challenges. The key contribution of the paper is an extended evaluation of the proposed architecture, by comparing it against established image segmentation benchmarks utilizing several metrics with cross-validation on the GIANA training dataset. Different experiments are described, including examination of various network configurations, values of design parameters, data augmentation approaches, and polyp characteristics. The reported results demonstrate the significance of the data augmentation, and careful selection of the method’s design parameters. The proposed method delivers state-of-the-art results with near real-time performance. The described solution was instrumental in securing the top spot for the polyp segmentation sub-challenge at the 2017 GIANA challenge and second place for the standard image resolution segmentation task at the 2018 GIANA challenge.
Tree Decomposition-based Approach for Compiling Independent Sets

Knowledge compilation is a method for compiling a knowledge base into an appropriate data structure, generally called tractable language. Graph substructure plays an important role in knowledge compilation and frontierbased search is known to be an efficient algorithm, in which computation time is bounded by the path-width of a graph. For some limited classes of graph structures, studies have shown that it can be improved and bounded by the branchwidth, however, the redesign of an algorithm for other classes does not appear to be straightforward. In this paper, we focus on the similarity between frontier-based search and dynamic programming on tree decomposition. Dynamic programming on tree decomposition has been intensely studied for varieties of problems on counting or optimization of graph substructures. However, to the best of our knowledge, they are rarely applied to knowledge compilation. Then, we show that dynamic programming for finding the size of the maximum independent set can be, by simple replacement, applied to the compilation of independent sets. Furthermore, we empirically show that our method can compile much faster than conventional frontier-based search in some instances, and it becomes several orders of magnitude faster especially when the tree-width is small compared to the path-width.
Managing News Overload (MNO): The COVID-19 Infodemic

A crucial area in which information overload is experienced is news consumption. Ever increasing sources and formats are becoming available through a combination of traditional and new (digital) media, including social media. In such an information and media rich environment, understanding how people access and manage news during a global health epidemic like COVID-19 becomes even more important. The designation of the current situation as an infodemic has raised concerns about the quality, accuracy and impact of information. Instances of misinformation are commonplace due, in part, to the speed and pervasive nature of social media and messaging applications in particular. This paper reports on data collected using media diaries from 15 university students in the United Arab Emirates documenting their news consumption in April 2020. Faced with a potentially infinite amount of information and news, participants demonstrate how they are managing news overload (MNO) using a number of complementary strategies. Results show that while consumption patterns vary, all diaries indicate that users’ ability to navigate the news landscape in a way that fulfils their needs is influenced by news sources; platform reliability and verification; sharing activity; and engagement with news.