Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of Computational Thinking in K-12 Context: Educational Practices, Limits and Possibilities - A Systematic Mapping Study

The computational thinking (CT) concept has been the basis for several studies in the K-12 educational context. However, there are many questions that need to be deepened to attend K-12 educational demands. One great challenge is concerning assessment. Aiming to contribute to understanding this issue we present a systematic mapping study. We found 46 articles that approach assessment in this context, and we extract this information. The vast majority are recent publications, there is no consensus in CT characteristics, blockbased languages are the most commonly used tool, instruments for assessment that are more used are pre and post-tests/questionnaires/surveys; samples sizes are usually small, and there is some psychometric rigor in just a few studies. Generally, the CT approaches were an isolated course or application, and their length of time was very different. Pedagogical foundations concerning the cognitive development stages and principles of knowledge structuration were rare. In addition, questions as “what has to be taught to the youngster? ” and “how to teach and to assess in alignment with K-12 goals? ” were not appropriately answered. Therefore, there are many research opportunities for the further development of this field.
60 GHz Substrate Integrated Waveguide-Fed Monolithic Grid Dielectric Resonator Antenna Arrays

Artificial grid dielectric resonator antenna (GDRA) arrays integrated with longitudinal slot-fed substrate integrated waveguide networks are demonstrated at V-band for wideband and high-gain applications. The effective permittivity of a 500 μm thick, low-permittivity polymethyl methacrylate substrate is locally increased by a factor of 22.4 by embedding groups of micro-sized rectangular metal inclusions, each group functioning as antenna elements and collectively functioning as an antenna array. The monolithic GDRA array mitigates the problem of individual element misalignment over the feed slots while providing precise interelement spacing. GDRA prototypes with 4 × 4 and 8 × 8 array elements fabricated and tested at 60 GHz demonstrate a −10 dB impedance bandwidth of 10.4% and 12.0% with broadside peak realized gain of 15.2 and 19.4 dBi.
A Fuzzy Approach for Data Quality Assessment of Linked Datasets

For several applications, an integrated view of linked data, denoted linked data mashup, is a critical requirement. Nonetheless, the quality of linked data mashups highly depends on the quality of the data sources. In this sense, it is essential to analyze data source quality and to make this information explicit to consumers of such data. This paper introduces a fuzzy ontology to represent the quality of linked data source. Furthermore, the paper shows the applicability of the fuzzy ontology in the process of evaluating data source quality used to build linked data mashups.
Learning ontologies for geographic entity matching and multi-sources data fusion

Geographic information integration and fusion from multi-sources geospatial data is still a challenge because of semantic heterogeneity of geospatial entity data. These will delay geospatial cooperation decision support and integrated assessment application for natural resources and environmental problem. Ontology as a conceptualization and knowledge representation of application domain have provided a potential and aided support for entity matching and information integration of multiple sources and heterogeneous data. However, many data sources may not provided ontology definition and formalization representation. This paper will provide a method of graph model based ontologies representation and ontologies learning from data sources schema. Entity matching and fusion with ontology support is also discussed. This paper has classified unary properties and binary properties in ontology representation. Binary semantic relations in ontologies are analyzed. Based on semantic relations, the Tree model and Network model of ontology representation are defined. Through analyzing entity data schema, semantic relations extraction and learning are also discussed in constitution of entity ontologies. The framework of entity fusion with ontology support has been designed. Finally entity matching and fusion methods are discussed.
An exploration of the pre-development phase in New Zealand small-to-medium enterprises

Developing successful products is difficult, with few companies being highly successful more than half the time. This presents a significant challenge for a product development team. This research was aimed at exploring the pre-development phase currently employed by small and medium enterprises (SMEs) from the New Zealand manufacturing industry. The purpose was to gauge the understanding and importance of this early stage in product development amongst practitioners from these SMEs, as the literature highlighted this as an area of weakness requiring empirical research. This study involved twenty-two SMEs using a questionnaire survey followed by personal interviews with some of the participants. The study found that the pre-development phase of New Zealand SMEs lacked awareness, commitment, and formality. Difficulties were also associated with carrying out the activities, worsened by the lack of skills in-house to do so.
A Probabilistic Approach based on a Finite Mixture Model of Multivariate Beta Distributions

Model-based approaches specifically finite mixture models are widely applied as an inference engine in machine learning, data mining and related disciplines. They proved to be an effective and advanced tool in discovery, extraction and analysis of critical knowledge from data by providing better insight into the nature of data and uncovering hidden patterns that we are looking for. In recent researches, some distributions such as Beta distribution have demonstrated more flexibility in modeling asymmetric and non-Gaussian data. In this paper, we introduce an unsupervised learning algorithm for a finite mixture model based on multivariate Beta distribution which could be applied in various real-world challenging problems such as texture analysis, spam detection and software modules defect prediction. Parameter estimation is one of the crucial and critical challenges when deploying mixture models. To tackle this issue, deterministic and efficient techniques such as Maximum likelihood (ML), Expectation maximization (EM) and Newton Raphson methods are applied. The feasibility and effectiveness of the proposed model are assessed by experimental results involving real datasets. The performance of our framework is compared with the widely used Gaussian Mixture Model (GMM).
Design and Testing of Simple, Electrically Small, Low-Profile, Huygens Source Antennas With Broadside Radiation Performance

The efficacy of a simple, electrically small, low-profile, Huygens source antenna that radiates in its broadside direction is demonstrated numerically and experimentally. First, two types of electrically small, near-field resonant parasitic (NFRP) antennas are introduced and their individual radiation performance characteristics are discussed. The electric one is based on a modified Egyptian axe dipole NFRP element; the magnetic one is based on a capacitively loaded loop NFRP element. In both cases, the driven element is a simple coax-fed dipole antenna, and there is no ground plane. By organically combining these two elements, Huygens source antennas are obtained. A forward propagating demonstrator version was fabricated and tested. The experimental results are in good agreement with their analytical and simulated values. This low profile, ~0. 05λ0, and electrically small, ka = 0.645, prototype yielded a peak realized gain of 2.03 dBi in the broadside direction with a front-to-back ratio of 16.92 dB. A backward radiating version is also obtained; its simulated current distribution behavior is compared with that of the forward version to illustrate the design principles.
Predictive Analytics - a Modern crystal ball? Answers from a Cash Flow Case Study

Companies use forecasts to predict deviations from their plans. Most of the forecasting processes rely on experience-based human judgement. Thus, they neglect predictive analytics which aims to be a "modern crystal ball. " We take a utility company and its cash flow planning process as our reference. Combining the best of both worlds, experience-based human judgement and predictive analytics, we lay out three imperatives for a more accurate and efficient cash flow prediction model as follows: (1) The art of modelling is about the right input factors and their meaningful combination. A deep business understanding and insights from a correlation matrix will help. (2) The business department should set a clear target for the model in advance. Transparency of the algorithms and delivering tangible results are more important than the last inch of accuracy. (3) Leveraging data decomposition and a meaningful method mix, a clean-sheet approach often takes less time and people than existing approaches.
A Simple Wide-Bandwidth and High-Gain Microstrip Patch Antenna With Both Sides Shorted

A simple microstrip patch antenna with wide bandwidth and high gain is presented and investigated. It is built on a long rectangular patch with both sides shorted. The antenna is investigated with a cavity model analysis. With a proper design, the TM<sub>03</sub> and TM<sub>11</sub> modes are excited and combined to achieve a wide bandwidth. In the operation band, the antenna provides a high-gain unidirectional beam around broadside. A prototype is fabricated and measured. Measured results show that the antenna with an overall size of 1.29<italic>λ</italic><sub>0</sub> × 0.73<italic>λ</italic><sub>0</sub> × 0.036<italic>λ</italic><sub>0</sub> (<italic>λ</italic><sub>0</sub> is the wavelength in free space) provides a wide bandwidth of 13.2% (|<italic>S</italic><sub>11</sub>| < −10 dB). In the operation band, the antenna has a stable high gain varying from 8.0 to 9.7 dBi and a low cross-polarization level of about −25 dB.
Convolutional Neural Network Applied to Code Assignment Grading

Thousands of students have their assignments evaluated by their teachers every day around the world while developing their studies in any branch of science. A fair evaluation of their schoolwork is a very challenging task. Here we present a method for validating the grades attributed by professors to students programming exercises in an undergraduate introductory course in computer programming. We collected 938 final exam exercises in Java Language developed during this course, evaluated by different professors, and trained a convolutional neural network over those assignments. First, we submit their codes to a cleaning process (by removing comments and anonymizing variables). Next, we generated an embedding representation of each source code produced by students. Finally, this representation is taken as the input of the neural network which classifies each label (corresponding to the possible grades A, B, C, D or F). An independent neural network is trained with source code solutions corresponding to each assignment. We obtained an average accuracy of 74.9% in a 10−fold cross validation for each grade. We believe that this method can be used to validate the grading process made by professors in order to detect errors that might happen during this process.
Enabling Clone Detection For Ethereum Via Smart Contract Birthmarks

The Ethereum ecosystem has introduced a pervasive blockchain platform with programmable transactions. Everyone is allowed to develop and deploy smart contracts. Such flexibility can lead to a large collection of similar contracts, i.e., clones, especially when Ethereum applications are highly domain-specific and may share similar functionalities within the same domain, e.g., token contracts often provide interfaces for money transfer and balance inquiry. While smart contract clones have a wide range of impact across different applications, e.g., security, they are relatively little studied. Although clone detection has been a long-standing research topic, blockchain smart contracts introduce new challenges, e.g., syntactic diversity due to trade-off between storage and execution, understanding high-level business logic etc. In this paper, we highlighted the very first attempt to clone detection of Ethereum smart contracts. To overcome the new challenges, we introduce the concept of smart contract birthmark, i.e., a semantic-preserving and computable representation for smart contract bytecode. The birthmark captures high-level semantics by effectively sketching symbolic execution traces (e.g., data access dependencies, path conditions) and maintain syntactic regularities (e.g., type and number of instructions) as well. Then, the clone detection problem is reduced to a computation of statistical similarity between two contract birthmarks. We have implemented a clone detector called EClone and evaluated it on Ethereum. The empirical results demonstrated the potential of EClone in accurately identifying clones. We have also extended EClone for vulnerability search and managed to detect CVE-2018-10376 instances.
CD47 Deficiency Protects Mice from Lipopolysaccharide-Induced Acute Lung Injury and Escherichia coli Pneumonia1

CD47 modulates neutrophil transmigration toward the sites of infection or injury. Mice lacking CD47 are susceptible to Escherichia coli (E. coli) peritonitis. However, less is known concerning the role of CD47 in the development of acute lung inflammation and injury. In this study, we show that mice lacking CD47 are protected from LPS-induced acute lung injury and E. coli pneumonia with a significant reduction in pulmonary edema, lung vascular permeability, and bacteremia. Reconstitution of CD47+/− mice with CD47−/− neutrophils significantly reduced lung edema and neutrophil infiltration, thus demonstrating that CD47+ neutrophils are required for the development of lung injury from E. coli pneumonia. Importantly, CD47-deficient mice with E. coli pneumonia had an improved survival rate. Taken together, deficiency of CD47 protects mice from LPS-induced acute lung injury and E. coli pneumonia. Targeting CD47 may be a novel pathway for treatment of acute lung injury.
Social Media and Social movements: a Case of women's March

Social media is a powerful medium to organize and mobilize social movements. In this study, we concentrate on the content of messages sent through social media during a particular social movement. Specifically, we focus on Twitter messages related to the Women’s March. Accordingly, we conduct a qualitative analysis of the content of the tweets posted during the marches held in January 2017 and January 2018. Through our analysis, we identify the different types of messages associated with the movement. These messages are classified through a set of categories and sub-categories. We found that most of the messages are used to motivate and mobilize individuals. Our research contributes to social movement and social media literature and enhances the understanding of how social media is used to motivate and mobilize people. The findings could also help social movement organizers in learning different ways in which social media can help them to frame their messages beyond motivation and mobilization.
Information Security in Agile Software Development Projects: a Critical Success factor Perspective

© 27th European Conference on Information Systems - Information Systems for a Sharing Society, ECIS 2019. All rights reserved. The importance of information security in software development projects is long recognised, with many comprehensive standards and procedures in use to provide assurance of information security. The agile development paradigm conflicts with traditional security assurance by emphasising the delivery of functional requirements and a reduction in structured and linear development styles. Through a series of thirteen qualitative interviews, this study identifies practices that address this problem which have been successfully adopted by agile practitioners. The findings present four categories of practices - organisational, team, project, and technical - and twelve critical success factors that should be explicitly considered by practitioners to assure agile security. The critical success factors provide a foundation for practitioners to strategically identify and develop best practices to embed information security in agile development projects. The identified categories also highlight the importance of agile security practices centring around individuals and culture and contributes to the literature by providing a representation of agile security practices that encompasses a broad range of focal areas.
Leveraging the Value of Data-Driven Service Systems in manufacturing: a Graph-based Approach

The utilization of data is a prominent goal for the digital transformation of business models. Related research provides a variety of insights on methods that can be applied in order to gather information as well as managerial perspectives and single use cases that demonstrate the operationalization of such and guide readers through necessary transformative steps. However, with the advent of digitized products and services, goods are decreasingly conceived and marketed exclusively. Rather, they interact with other goods on an informational level and retrieve additional value from their systemic integration. We argue that the utilization of such "data-driven service systems" requires a holistic perspective that regards the complete "digital value chain" from the initial data retrieval to the execution of contextbased services. This contribution presents a graph-based approach for the integration of event information in service activities in order to leverage the conceptual value. We pilot the transformation of a maintenance process in Manufacturing to evaluate our approach. It turned out that the exemplary process could be simplified from 8 to 4 actions that remained to be executed. Concluding, we discuss the business value and the general applicability of the approach for further use cases.
Teaching Internet of Things (IoT) Literacy: A Systems Engineering Approach

The Internet of Things (IoT) invades our world with billions of smart, interconnected devices, all programmed to make our lives easier. For educators, teaching such a vast and dynamic field is both a necessity and a challenge. IoT-relevant topics such as programming, hardware, networking and artificial intelligence are already covered in core computing curricula. Does this mean that fresh graduates are well prepared to tackle complex IoT problems? Unfortunately, nothing could be further from the truth. The problem is that IoT devices are complex systems, where software, hardware, and humans interact with each other. From this interaction, unique behavior and hazardous situations can emerge that might easily stay undetected, unless systems are analyzed as a whole. This paper presents two differently flavored courses that teach IoT using a holistic, system-centric approach. The first is a broad introduction to Pervasive Computing, focused on the intelligence of "Things". The second is an advanced course that zooms on the process of testing a software-intensive system. The key characteristics of our approach are : (1) teaching only the bare essentials (topics needed for end-to-end engineering of a smart system), (2) a strong, hands-on project component, using microcontroller-based miniature systems, inspired by real-life, and (3) a rich partnership with industry and academic idea incubators. Positive student evaluations gathered during the last five years demonstrate that such an approach brings engagement, self-confidence and realism in IoT classrooms. We believe that this success can be replicated in other courses, by shifting the focus on different IoT-relevant aspects.
RESPONSE TO TV COMMERCIALS : AN EMPIRICAL INVESTIGATION FOR DIGITAL-NATIVE BRANDS

This research investigates the effect of TV advertising on website traffic and sales for new and growing e-commerce and online services brands. We created a Python-based extraction and transformation pipeline to join data from four different sources and to establish one of the most comprehensive datasets in this field. It contains approximately 300,000 TV ad airings in 2016/17 of around 800 unique creatives and associated website-tracking and brand awareness data of 23 consumer-facing digital-native brands. We draw on signaling and information processing theory to assess whether companies can boost online consumer activity by aligning creative cues in TV commercials with consumers’ awareness of their brand. Our empirical analyses reveal that informative and imagery-heavy ads are more powerful for lesser-known brands, while better-known brands achieve a higher uplift with emotional commercials. This provides compelling insights for marketing practitioners on how to conceive their TV ads to maximize online response. Our work further shows that e-commerce and media players can generate a competitive advantage by integrating disparate data sources and conducting in-depth analytics on the types of ads that achieve the highest impact for their brands as well as by developing and employing the associated information systems infrastructure to do so efficiently.
An Annotation Model on End-to-End Chest Radiology Reports

Annotating radiographic images with tags is an indispensable preliminary work in computer-aided medical research, which requires professional physician participated in and is quite time-consuming. Therefore, how to automatically annotate radiographic images has become the focus of researchers. However, image report texts, containing crucial radiologic information, have not to be given enough attention for images annotation. In this paper, we propose a neural sequence-to-sequence annotation model. Especially, in the decoding phase, a probability is first learned to copy existing words from report texts or generate new words. Second, to incorporate the patient’s background information, “indication” section of the report is encoded as a sentence embedding, and concatenated with the decoder neural unit input. What’s more, we devise a more reasonable evaluation metric for this annotation task, aiming at assessing the importance of different words. On the Open-i dataset, our model outperforms existing non-neural and neural baselines under the BLEU-4 metrics. To our best knowledge, we are the first to use sequence-to-sequence model for radiographic image annotation.
Stacked Gated Nanofluidic Logic Gate Membrane

The objective of this work is to model and experimentally verify the molecular transport of charged molecules in a novel AND nanofluidic logic gate. Two nanoporous membranes with gate-insulator layers were placed between two reservoirs with asymmetric target molecule concentrations, controlled by independent electrical gates at both openings. When the dimension of the fluidic channel is comparable to the thickness of the electrical double layer, the potential generated by gating becomes significant, which allows control of diffusive transport of charged molecules through the nanochannel modulated by electrostatic repulsion and attraction. The simulation and experimental results using two independent gates show that the stacked gate structure implemented a nanofluidic AND logic gate for the charged molecular transport.