The pervasiveness of Computer Science (CS) in today’s digital society and the extensive use of computational methods in other sciences call for its introduction in the school curriculum. Hence, ...Computer Science Education is becoming more and more relevant. In CS K-12 education, computational thinking (CT) is one of the abused buzzwords: different stakeholders (media, educators, politicians) give it different meanings, some more oriented to CS, others more linked to its interdisciplinary value. The expression was introduced by two leading researchers, Jeannette Wing (in 2006) and Seymour Papert (much early, in 1980), each of them stressing different aspects of a common theme. This paper will use a historical approach to review, discuss, and put in context these first two educational and epistemological approaches to CT. We will relate them to today’s context and evaluate what aspects are still relevant for CS K-12 education. Of the two, particular interest is devoted to “Papert’s CT,” which is the lesser-known and the lesser-studied. We will conclude that “Wing’s CT” and “Papert’s CT,” when correctly understood, are both relevant to today’s computer science education. From Wing, we should retain computer science’s centrality, CT being the (scientific and cultural) substratum of the technical competencies. Under this interpretation, CT is a lens and a set of categories for understanding the algorithmic fabric of today’s world. From Papert, we should retain the constructionist idea that only a social and affective involvement of students into the technical content will make programming an interdisciplinary tool for learning (also) other disciplines. We will also discuss the often quoted (and often unverified) claim that CT automatically “transfers” to other broad 21st century skills. Our analysis will be relevant for educators and scholars to recognize and avoid misconceptions and build on the two core roots of CT.
Social Media Technology and Government Transparency Bertot, John Carlo; Jaeger, Paul T; Munson, Sean ...
Computer (Long Beach, Calif.),
2010-Nov., 2010-11-00, 20101101, Letnik:
43, Številka:
11
Journal Article
Recenzirano
Researching and evaluating social media initiatives is essential to encourage usage, continually improve the tools and policies governing their use, and develop governance processes that incorporate ...social media participation in its many forms.
Information security risk assessment methods have served us well over the past two decades. They have provided a tool for organizations and governments to use in protecting themselves against ...pertinent risks. As the complexity, pervasiveness, and automation of technology systems increases and cyberspace matures, particularly with the Internet of Things (IoT), there is a strong argument that we will need new approaches to assess risk and build trust. The challenge with simply extending existing assessment methodologies to IoT systems is that we could be blind to new risks arising in such ecosystems. These risks could be related to the high degrees of connectivity present or the coupling of digital, cyber-physical, and social systems. This article makes the case for new methodologies to assess risk in this context that consider the dynamics and uniqueness of the IoT while maintaining the rigor of best practice in risk assessment.
highlights•Social spam evolution is leading to a marked deterioration in the performance of state-of-the-art supervised classifiers.•The Markov Random Fields formalism allows a hybrid social spam ...detection model that exploits both users features and their content-based similarity.•Users content can be exploited to define robust similarity measures.•Biased and inaccurate prior predictions on users classes can be effectively used in the context of probabilistic graphical models as demonstrated by the significant increase in recall obtained by the proposed approach.
The problem of social spam detection has been traditionally modeled as a supervised classification problem. Despite the initial success of this detection approach, later analysis of proposed systems and detection features has shown that, like email spam, the dynamic and adversarial nature of social spam makes the performance achieved by supervised systems hard to maintain. In this paper, we investigate the possibility of using the output of previously proposed supervised classification systems as a tool for spammers discovery. The hypothesis is that these systems are still highly capable of detecting spammers reliably even when their recall is far from perfect. We then propose to use the output of these classifiers as prior beliefs in a probabilistic graphical model framework. This framework allows beliefs to be propagated to similar social accounts. Basing similarity on a who-connects-to-whom network has been empirically critiqued in recent literature and we propose here an alternative definition based on a bipartite users-content interaction graph. For evaluation, we build a Markov Random Field on a graph of similar users and compute prior beliefs using a selection of state-of-the-art classifiers. We apply Loopy Belief Propagation to obtain posterior predictions on users. The proposed system is evaluated on a recent Twitter dataset that we collected and manually labeled. Classification results show a significant increase in recall and a maintained precision. This validates that formulating the detection problem with an undirected graphical model framework permits to restore the deteriorated performances of previously proposed statistical classifiers and to effectively mitigate the effect of spam evolution.
Although Sweden is one of the most digitalized countries and the Swedish population’s use of the internet is among the most studied in the world, little is known about how Swedes with disabilities ...use internet. The purpose of this study is to describe use of and perceived difficulties in use of the internet among people with disabilities and to explore digital divides in-between and within disability groups, and in comparison with the general population. This is a cross-sectional survey targeting the same issues as other nationwide surveys but adapted for people with cognitive disabilities. Participants were recruited from May to October 2017 by adaptive snowball sampling. The survey comprised questions on access to and use of devices, and use of and perceived difficulties in use of internet. A total of 771 people responded to the survey, representing 35 diagnoses/impairments. Larger proportions of people with autism, ADHD and bipolar disorder reported using internet than other disability groups. Women with autism used the internet more than any other disability group, and women with aphasia used the internet the least. People with disabilities related to language and understanding reported more difficulties using internet than other disability groups. Larger proportions of participants than the general Swedish population reported not feeling digitally included. In many but not all disability groups, larger proportions of men than women reported not feeling digitally included. Our findings show that there are differences in digital inclusion between sub-groups of diagnoses/impairments. Thus, disability digital divides are preferably investigated by sub-grouping disabilities, rather than studied as one homogeneous group.
This study investigated the core motivations for adopting smart home services and explored the approaches and processes through which the motivations were incorporated with the original technology ...acceptance model (TAM) and the acceptance of the services. To achieve this purpose, an Internet survey was conducted in South Korea. The data (
N
= 799) from the survey were analyzed using structural equation modeling and confirmatory factor analysis. The results suggested that the perceived compatibility, connectedness, control, system reliability, and enjoyment of smart home services were positively related to the users’ intention to use the services, whereas there was a negative association between the perceived cost and usage intention. The structural results also provided evidence of the validity of the original TAM. Although smart home services have attracted users’ interest in the housing context, only a few studies have examined how the users’ intention to use the services is motivated. The present study represents an initial step to explore the process of adopting smart home services with potential future research areas.
In this article, we assess the potential of depth imaging systems for 3D measurements in the context of plant phenotyping. We propose an original algorithm to segment depth images of plant from a ...single top-view. Various applications of biological interest involving for illustration rosebush, yucca and apple tree are then presented to demonstrate the practical interest of such imaging systems. In addition, the depth camera used here is very low cost and low weight. The present results therefore open interesting perspectives in the direction of high-throughput phenotyping in controlled environment or in field conditions.
BB-UNet: U-Net With Bounding Box Prior Rosana, El Jurdi; Petitjean, Caroline; Honeine, Paul ...
IEEE journal of selected topics in signal processing,
10/2020, Letnik:
14, Številka:
6
Journal Article
Recenzirano
Odprti dostop
Medical image segmentation is the process of anatomically isolating organs for analysis and treatment. Leading works within this domain emerged with the well-known U-Net. Despite its success, recent ...works have shown the limitations of U-Net to conduct segmentation given image particularities such as noise, corruption or lack of contrast. Prior knowledge integration allows to overcome segmentation ambiguities. This paper introduces BB-UNet (Bounding Box U-Net), a deep learning model that integrates location as well as shape prior onto model training. The proposed model is inspired by U-Net and incorporates priors through a novel convolutional layer introduced at the level of skip connections. The proposed architecture helps in presenting attention kernels onto the neural training in order to guide the model on where to look for the organs. Moreover, it fine-tunes the encoder layers based on positional constraints. The proposed model is exploited within two main paradigms: as a solo model given a fully supervised framework and as an ancillary model, in a weakly supervised setting. In the current experiments, manual bounding boxes are fed at inference and as such BB-Unet is exploited in a semi-automatic setting; however, BB-Unet has the potential of being part of a fully automated process, if it relies on a preliminary step of object detection. To validate the performance of the proposed model, experiments are conducted on two public datasets: the SegTHOR dataset which focuses on the segmentation of thoracic organs at risk in computed tomography (CT) images, and the Cardiac dataset which is a mono-modal MRI dataset released as part of the Decathlon challenge and dedicated to segmentation of the left atrium. Results show that the proposed method outperforms state-of-the-art methods in fully supervised learning frameworks and registers relevant results given the weakly supervised domain.
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