Reviewerly/exHarmony huggingface dataset:
The Attitudes of Teachers-in-Training Towards Information Literacy Skills and the Inclusion of Mobile Devices in the Process of Teacher Education

The main focus of this paper is to analyze the relation between information literacy and the use of mobile devices in the process of learning-teaching in university students, specifically, teachers-in-training. For this purpose, a group of students belonging to the last semester of the Education Degree (N = 44) at the University of Granada was selected for a qualitative-descriptive study, based on a focus group, using the techniques of the thinking aloud and the semi-structured interviews; and for a brief quantitative questionnaire founded in a Likert-Scale. With these two procedures we tried to measure attitudes towards information literacy and the influence that mobile devices exert on its acquisition. Both results (qualitative and quantitative) were compared in order to check the consistency of the information offered by the students. After we analyzed the data, we conclude the following: 1. There is a generalized use of mobile devices in the process of learning-teaching. The use of digital technologies for communication and learning purposes is obviously a part of the identity of young people and, in particular, of university students. 2. The increase of these devices is related to a change of attitudes, which includes distraction, use of new technologies for other purposes (different to academics) and lack of critical outlook. 3. Age is a relevant factor for the development of attitudes and skills related to critical and independent thinking. Nevertheless, significant differences were not observed as far as the use of mobile and the acquisition of digital information skills were concerned. 4. To a greater or a lesser extent, students demand the use of new technologies combined with a traditional methodology, on the basis of master classes, discussion groups, dialogue and interactions, and practicum. It is also remarkable that an excessive use of mobile devices may hinder or overwhelm the acquisition of literacy skills. 5. Data analysis identified gaps in the information literacy of teachers-in-training despite their constant access to information via mobile devices. Therefore, we propose that appropriate information literacy training is a key priority in teacher education. 6. Concerning the use of mobile technologies in the classroom, this research suggests both a positive impact and also some disadvantages.
Data Sharing in Social Sciences: Case Study on Charles University

Data sharing is an important building block for data-intensive science, but it is highly dependent on researchers’ behavior. According to our recent research, both academics and doctoral students at Charles University are willing to share data, but at the same time they expressed various concerns about data sharing. In our present study, we focus on the group of social science researchers. We use the data from a questionnaire and semi-structured interviews. We collected 1,434 completed questionnaires, from which 279 fall into the social sciences category. It seems that social scientists are less willing to share their data; their data is more often unavailable; and they are more concerned about potential ethical and legal problems. Interviews with select social scientists provided more details on the motivators and inhibitors of data sharing, with data character and content seen as the greatest obstacles to data sharing.
O-PCF algorithm for one-class classification

One-class classification, or outlier detection, is of great importance when data can be properly obtained from only one target class. The problem has many applications in various areas when the outlier class, defined as the complementary set to the target class, is absent. In this paper, we develop a novel one-class classification algorithm called the one-class polyhedral conic functions (O-PCF) algorithm. In this algorithm, the decision boundary for the target class is defined by PCFs' level sets. The level set of a PCF is a convex polyhedron; thus, only convex decision boundaries can be obtained with one PCF. However, the target class may have a non-convex structure. Thus, the O-PCF algorithm divides the target class into k clusters and obtains a PCF for each cluster. O-PCF constructs the final classifier as the minimum of k PCFs to generate non-convex separating surfaces. The performance of the O-PCF algorithm is presented in comparison with other methods in the literature. The test results lead us to conclude that the O-PCF algorithm outperforms the other methods in many cases.
A Novel Method for Polar Form of Any Degree of Multivariate Polynomials with Applications in IoT

Identification schemes based on multivariate polynomials have been receiving attraction in different areas due to the quantum secure property. Identification is one of the most important elements for the IoT to achieve communication between objects, gather and share information with each other. Thus, identification schemes which are post-quantum secure are significant for Internet-of-Things (IoT) devices. Various polar forms of multivariate quadratic and cubic polynomial systems have been proposed for these identification schemes. There is a need to define polar form for multivariate dth degree polynomials, where d ≥ 4 . In this paper, we propose a solution to this need by defining constructions for multivariate polynomials of degree d ≥ 4 . We give a generic framework to construct the identification scheme for IoT and RFID applications. In addition, we compare identification schemes and curve-based cryptoGPS which is currently used in RFID applications.
A hybrid algorithm of interval type-2 fuzzy logic system and generalized adaptive resonance theory for medical data classification

The hybrid of artificial neural network (ANN) and fuzzy logic system (FLS) can expend itself dynamically in a strong discovery of explicit knowledge to solve classification and regression problems with new input patterns. In this paper, a hybrid of Generalized Adaptive Resonance Theory (GART) and i nterval type-2 fuzzy logic system (IT2FLS) algorithm is proposed, and named as Generalized Adaptive Resonance Theory and interval type-2 fuzzy logic system (GART-IT2FLS). The GART is a combination of adaptive resonance theory network (ART) and Generalized Regression Neural Network (GRNN). GART is capable to deal with classification task effectively. However, type-2 fuzzy sets (T2 FS) is used to represent and model the uncertainties on inputs. The performance evaluation of GART-IT2FLS algorithm in three medical datasets has proven that GART-IT2FLS is capable to learn incrementally without plasticity-stability dilemma, and model uncertainties in medical datasets. The inferences of GAR-IT2FLS in these applications are discussed. The performance results show that GART-IT2FLS has obtained a comparable number of rules. The Wisconsin Breast Cancer and Heart Disease experiments demonstrated GART-IT2FLS has improved the testing accuracies.
Energy-efficient crypto acceleration with HW/SW co-design for HTTPS

Entering the Big Data era leads to the rapid development of web applications which provide high-performance sensitive access on large cloud data centers. HTTPS has been widely deployed as an extension of HTTP by adding an encryption layer of SSL/TLS protocol for secure communication over the Internet. To accelerate the complex crypto computation, specific acceleration instruction set and hardware accelerator are adopted. However, energy consumption has been ignored in the rush for performance. Actually, energy efficiency has become a challenge with the increasing demands for performance and energy saving in data centers. In this paper, we present the EECA, an Energy-Efficient Crypto Acceleration system for HTTPS with OpenSSL. It provides high energy-efficient encryption through HW/SW co-design. The essential idea is to make full use of system resource to exert the superiorities of different crypto acceleration approaches for an energy-efficient design. Experimental results show that, if only do crypto computations with typical encryption algorithm AES-256-CBC, the proposed EECA could get up to 1637.13%, 84.82%, and 966.23% PPW (Performance per Watt) improvement comparing with original software encryption, instruction set acceleration and hardware accelerator, respectively. If considering the whole working flow for end-to-end secure HTTPS based on OpenSSL with cipher suite ECDHE-RSA-AES256-SHA384, EECA could also improve the energy efficiency by up to 422.26%, 40.14% and 96.05% comparing with the original Web server using software, instruction set and hardware accelerators, respectively.
Construction of public policy of higher education with participation of the academic community, Colombian, case for recognition of virtual and distance learning programs

As of this date, 8641 academic programs are authorized by Colombia's National Ministry of Education. Of these, 7923 are in-person, 404 are virtual, and 314 are offered at distance. Of the total number of virtual and at-distance programs, only 26 (3.62%) are accredited in the high-quality rank. This low accreditation figure is attributed to a lack of evaluation instruments encompassing criteria to recognize high quality of virtual and at-distance programs. Given the previous, the academic community joined efforts to design and develop the program "Collective construction of criteria aimed at enabling recognition of high quality virtual and at-distance learning programs in Colombia". Regional workshops were conducted that succeeded in gathering representatives of around 100 Higher Education Institutions representing nearly 95% of institutions offering virtual and at-distance learning programs in Colombia. The product of those workshops was consolidated and brought to the consideration of the Principals of those institutions for their respective validation. The validated version was submitted to the National Ministry of Education as a technical document that will support the public policy that rules accreditation of virtual and at-distance learning programs in Colombia.
Grammatical Facial Expression Recognition Basing on a Hybrid of Fuzzy Rough Ant Colony Optimization and Nearest Neighbor Classifier

Humans use facial expressions in many contexts to communicate their ideas or weigh their emotions. Deaf people depend on these expressions mainly in daily communications. They use the facial expressions to add the grammatical meaning for sentences of similar words. Therefore, developing smart systems to recognize facial expressions becomes a necessity. The main obstacle comes from the uncertainty and ambiguity of grammatical facial decisions. Hence, fuzzy and fuzzy rough artificial intelligent algorithms formulate feasible solutions to make decisions in such situations. This paper presents a hybrid of fuzzy rough feature selection inspired by ANT Colony Optimization (FRFS-ACO) and fuzzy rough nearest neighbor (FRNN) classification algorithms to decide about different facial expressions. The proposed hybrid is compared to other artificial algorithms and hybrids to judge its accuracy and efficiency. The experiments are accomplished using a standard grammatical facial expressions data set with nine different emotions recorded by Microsoft Kinect sensor and kept on the UCI machine learning repository. The experiments and comparisons clarified that the proposed hybrid provide feasible average accuracy level of 93.7% and dramatically reduced the required classification time.
XREmul

EXtended Reality(XR), which includes the concepts of virtual reality, augmented reality and mixed reality, is a promising technology for the research community and also the commercial domain in the sense that it can open a variety of new applications in a novel computing environment. Most XR applications are "interactive" and this interactivity is realized using immersive equipments such as XR headsets and controllers. Compared to applications in traditional computing environments, an XR application requires more sophisticated and careful design given that diverse dimensions of interaction is allowed and also because users can be more sensitive to the application/functional quality due to the higher levels of immersion.
Image Authentication for Permissible Cropping

In a digital society, it is not easy to achieve a balance between privacy and authentication. Privacy requires some modifications of the original image and signed data, but image authentication needs to ensure its integrity as much as possible. The most common method of privacy protecting is deleting some sensitive data in the original data. In this paper, we propose a practical scheme of image authentication for permissible cropping operation, using a textbook RSA signature scheme and a message commitment scheme. The security of our scheme is implied by the security of underlying cryptographic primitives. Experimental results show that the proposed scheme is practical and can be embedded in some on-line systems.
Practical design and implementation methods for Kalman filtering for mission critical applications

NAVIGATION is a quarterly journal published by the Institute of Navigation. The journal publishes original, peer-reviewed articles on all aspects of positioning, navigation, and timing. The journal also publishes selected technical notes and survey articles, as well as papers of exceptional quality drawn from the Institute's conference proceedings.
Introduction: Strategic uses of politeness formulae. Analytical approaches and theoretical accounts

The present study investigates functions of ‘laugh’ particles like haha, hehe or hihi in a corpus of German WhatsApp text messages. It contributes to research on humor, ‘laugh’ particles and emojis in mobile messaging chats by examining the different stances the interlocutors take when they use ‘laugh’ particles in WhatsApp postings and the role emojis play in these postings. Based on an interactional approach to the analysis of WhatsApp chats, the study shows that ‘laugh’ particles are prototypically deployed in a posting-initial position and that they relate to previous postings or utterances ‘Laugh’ particles can be used to establish or support a humorous joking modality (laughing with). Particularly in group chats users can cooperatively turn one participant into the target of their ‘laughter’ (laughing at). ‘Laugh’ particles and emojis are closely connected, as emojis help to contextualize these different ‘laughter’ stances.
The Penguin – On the Boundary Between Pet and Machine. An Ecological Perspective on the Design of Assistive Robots for Elderly Care

Following current demographical trends, the aging population has emerged as a main target group for the development of assistive robots [16]. In current studies, assistive robots are seen as assistants, butlers or companion pets [2, 5]. These roles seem to assume an anthropomorphic or zoomorphic metaphor for the role of robots, acknowledging them intelligence and independence in performing daily tasks. In this paper we wish to reflect on the roles that assistive robots could play in elderly care, building on results gathered from a research through design investigation that we conducted as part of the SMOOTH (Seamless huMan-robot interactiOn fOr THe support of elderly people: www.smooth-robot.dk (Last seen 15/02/2019)) project. Our results suggest that the participants to our study seemed divided between understanding our robot as a tool but also as an intelligent being capable of social interaction. Therefore, we propose that assistive robots might be playing an ambiguous, evolving role in between that of a tool with a specific purpose and an intelligent being, like a pet, not equal and unthreatening to their human counterparts.
Fundamental Visual Concept Learning From Correlated Images and Text

Heterogeneous web media consists of many visual concepts, such as objects, scenes, and activities, which cannot be semantically decomposed. The task of learning fundamental visual concepts (FVCs) plays an important role in automatically understanding the elements that compose all visual media, as well as in applications of retrieval, annotation, and so on. In this paper, we formulate the problem of FVC learning and propose an approach to this problem called neighboring concept distributing (NCD). Our approach models all data using a concept graph, which considers the visual patches in images as nodes and generates the inter-image edges between visual patches in different images and the intra-image edges between visual patches in the same image. The NCD approach distributes semantic information from images to visual patches based on measurements over the concept graph, including fitness, distinctiveness, smoothness, and sparseness, without any pre-trained concept detectors or classifiers. We analyze the learnability of the proposed approach and find that, under some conditions, all concepts can be correctly learned with an arbitrarily high probability as the size of the data increases. We demonstrate the performance of the NCD approach using three public datasets. The experimental results show that our approach outperforms state-of-the-art approaches when learning visual concepts from correlated media.
Online Adaptive Approximate Stream Processing With Customized Error Control

In approximate processing on stream data, most works focus on how to approximate online arrival data. However, the efficiency of approximation needs to consider multiple aspects. Generally, customers submit their requests with specific quality requirements (e.g., maximum error). This raises a critical problem that online quality control is required to meet the desired quality of service. Since the continuous arriving data may not be entirely stored and needs to be processed immediately, it brings the difficulty of acquiring knowledge online which significantly affects the quality of results. To address these problems, we present an online adaptive approximate processing framework with a delicate combination of data learning, sampling, and quality control. We first design an online data learning strategy for stream data. With the real-time learning results, we propose a dynamic sampling strategy that switches to different sampling methods based on the change of the load. Finally, we present a double-check error control strategy to monitor and correct large errors. Each operation module is correlated through online learning and feedback. The experiments with both synthetic and real-world datasets show that the proposed approximate framework is not only applicable to different data distributions but also provides a customized error control.
Participatory design for selection of icons to represent daily activities of living for a vision-based rehabilitation-cum-assistance system for locked-in patients

Purpose: The majority of rehabilitation systems for locked-in patients are used for therapeutic purposes. However, round-the-clock assistance and support are essential after discharge from hospitals or nursing homes. This inspired us to develop a round-the-clock rehabilitation-cum-assistance system operated by eye gazes of locked-in patients. To do this, we aimed to identify the essential daily activities of living for locked-in patients and represent these activities using the most universally acceptable icons/images on the interface for the rehabilitation-cum-assistance system.Method: Activities were selected from available conventions and literature with advice from a local physiotherapy centre. Simple arithmetic averages and weighted averages of recognition rates for different icons representing different daily activities were calculated as per ISO 3864. Universal acceptability of the icons was compared based on responses from 72 locked-in patients across five different age groups. The final icons or images were then selected.Results: Three icons were categorized as identifiable with overall average recognition rates above 66.7%. Icons with average recognition rates 30-60% were considered to have "medium" recognisability and 21 icons were in this category. The average recognition rates of six icons were below 30% and were not acceptable. An overall recognition efficiency of 91% was achieved for participants from all age groups.Conclusions: The most preferred and unambiguous icons or images representing the essential daily activities of living were identified for use on the interface of our rehabilitation-cum-assistance system for round-the-clock operation.Implications for rehabilitationPerforming some of the daily activities by locked-in patients himself/herself is essential for self-independence as well as measures the level of regular improvement.Assistive technologies have huge potential for application for the purpose, especially the vision-based systems.Present technology is useful for development of a vision-based rehabilitation-cum-assistance system for 24 × 7 assistance for the locked-in patients.A new approach for real-time, user-based field evaluation of icons representing daily activities of living using statistical method of averaging of the recognition rates.
Harnessing Cloud Computing Services for E-Learning Systems in Higher Education

The use of e-learning suggests the use of ICT to enhance the quality of learning and teaching. However, many higher education institutions, does not have e-learning platforms, resources and infrastructure necessary to implement this type of training. This is due to the need for high cost of basic infrastructure and applications challenges related projects it has to face. This article puts forward an overview on what is the current state of the use of cloud computing in e-learning in higher education context, where the use of computers is increasingly intensive. The article analyzes e-learning systems challenges and trends, the convenience of cloud computing for e-learning and the key benefits of e-learning on the cloud. The article exposes also some application solutions using cloud computing in e-learning for higher education, by presenting the most common architecture that has been adopted. Finally, this article discusses issues related to the implementation of cloud-based e-learning systems and presents some potential ways to overcome them.
Evaluation Study Outcome of Augmented Reality Technology for Solving Engineering Problems in UNITEN

In this article, a study was done to compare the preferences of UNITEN students in using augmented reality technology user interface applications to solve selected engineering problems. Cross sectional study design and a Wilcoxon-Signed Rank Test approach was adopted to analyze the difference in the rankings of the engineering applications. Within a controlled environment, this research further applies “Trials within the same session but with breaks between tasks” an affirmed reliable method in measuring learnability that has been rarely explored by any related works locally. The results were captured through descriptive statistical analysis. The findings provided reliable evidences that multiple function user interface (MFIT) is more effective than the tangible user interface (TUI) for engineering students. Simultaneously, this research also presents evidences that MFIT is better than TUI in the engineering problem solving statistically.