Miracle-dz/UniFAR-FTUs huggingface dataset:
The Frequency of Subtypes of Respiratory Influenza Viruses Isolated in Busan during 2013 Season

A better understanding of human influenza seasonality is useful to inform the timing and composition of vaccine recommendations and monitor the emergence of new virus variants. In 2013 season, we analyzed influenza virus specimen in 1,504 hospitalized children to identify three kinds of respiratory influenza virus A (FluH1N1, and FluH3N2) and FluB. Identification of viruses was used the indirect fluorescent test (IFA) with FITC-conjugated anti-mouse immunoglobulin and hem agglutination inhibition test (HI test). We analyzed the positive rate, annual frequencies according to the retrospective review of medical records. Positive respiratory viruses were isolated in 132 (8.8%) out of 1,504 patients examined. Of 848 influenza virus A samples, 82 (8.8%) had positive. Of 82 positive influenza A virus samples, number of influenza H1N1 subtype was 9 (2.6%) and number of influenza H3N2 subtype was 73 (14.5%). The positive influenza virus B was 7.6% (50/656). Positive respiratory viruses on February and March were detected 18.6% and 16.3%, respectively. The frequencies of detection rates between males and females were similar to each other. As for the age distribution, the high detection rates were indicated in children at the age of two to five. Whenever children are together, there is a chance of spreading infections.
HOW CONSTRUCTION OF DIAGRAM EFFECT ADOLESCENT STUDENT PERFORMANCE IN SCIENCE EDUCATION?

In class room environment, complex ideas are commonly represented through diagrams, images and models used in every day situation. However effective use of pictorial presentation is a complex process. Research establishes that pictorial diagram presentation leads to heavy cognitive load on an individual to perceive or conceive when ideas are represented through pictorial diagram presentation based learning. This article investigates which pictorial presentation (pre-constructed diagrams versus self-constructing diagrams) effects one’s cognitive overload and learning outcome. This study also finds how different levels of prior knowledge effects learning outcome. The result of this study indicates that students learned better in self-constructing diagram condition than the pre-constructed diagram condition. This study explores how two kind of visual representation affects cognitive overload of the individual and subsequently, influence their achievement and performance.
Sleeping Eyes: Experiencing Narcolepsy Through the Duality of Virtual Embodiment

The aim of this art-based research is to explore the duality of embodiment and presence in virtual reality (VR) through a narrative about the South Korean narcoleptic artist, Sungeun Lee. Narcolepsy is a neurological disorder where a person falls asleep unexpectedly. A narcoleptic struggles with a blurred and unsure sense of self, as well as confusion between reality and dreaming. We used VR to simulate his traumatized dream world and symptoms of narcolepsy. However, we also investigated the fundamental duality of embodiment/disembodiment in the virtual space and its parallels in the dream world. A non-linear narrative and generative poetic aesthetics driven by biometric feedback were applied to represent a surreal and uncertain sense of self. Gesture-based navigable interaction was used to connect people with their physical bodies, the virtual environment, and Sungeun's experiences.
ForceStamps: Fiducial Markers for Pressure-sensitive Touch Surfaces to Support Rapid Prototyping of Physical Control Interfaces

We present ForceStamps, fiducial markers for supporting rapid prototyping of physical control interfaces on pressure-sensitive touch surfaces. We investigate marker design options for supporting various physical controls, with focusing on creating dedicated footprints and maintaining the structural stability. ForceStamps can be persistently tracked on surfaces along with the force information and other attributes. Designers without knowledge of electronics can rapidly prototype physical controls by attaching mechanisms to ForceStamps, while manipulating the haptic feedback with buffer materials. The created control widgets can be spatially configured on the touch surface to make an interface layout. We showcase a variety of example controls created with ForceStamps. In addition, we report on our analysis of a two-day musical instrument design workshop to explore the affordances of ForceStamps for making novel instruments with diverse interaction designs.
Generic Dynamic Data Outsourcing Framework for Integrity Verification

Ateniese et al. proposed the Provable Data Possession (PDP) model in 2007. Following that, Erway et al. adapted the model for dynamically updatable data and called it the Dynamic Provable Data Possession (DPDP) model. The idea is that a client outsources her files to a cloud server and later challenges the server to obtain a proof of the integrity of her data. Many schemes have later been proposed for this purpose, all following a similar framework. We analyze dynamic data outsourcing schemes for the cloud regarding security and efficiency and show a general framework for constructing such schemes that encompasses existing DPDP-like schemes as different instantiations. This generalization shows that a dynamic outsourced data integrity verification scheme can be constructed given black-box access to an implicitly-ordered authenticated data structure. Moreover, for blockless verification efficiency, a homomorphic verifiable tag scheme is also needed. We investigate the requirements and conditions these building blocks should satisfy, using which one may easily check the applicability of a given building block for dynamic data outsourcing. Our framework serves as a guideline/tutorial/survey and enables us to provide a comparison among different building blocks that existing schemes employ.
Gear VR and leap motion sensor applied in virtual rehabilitation for manual function training: an opportunity for home rehabilitation

Virtual reality systems using gestural interfaces are becoming common in treatment of people with disabilities. One reason for this popularity is due to the availability of low cost tracking sensors. We present a serious game interface that uses the Leap Motion sensor (coupled to Gear VR headset) for manual function rehabilitation. A monitoring system was developed so that the patient can use the game at home without losing their data. One therapist and five people with Cerebral Palsy used the system in a virtual rehabilitation. We investigated the individual performance of each participant (through counting points by time), satisfaction (through the System Usability Scale - SUS), cybersickness symptoms (through the Simulation Sickness Questionnaire - SSQ) and the level of active participation measured with the Pittsburgh Rehabilitation Participation Scale (PRPS). Tests allow that the system has a good acceptance to be used in rehabilitation.
DIVIDI2: reinforcing divided attention in children with AD/HD through a mobile application

The Attention Deficit/Hyperactivity Disorder (AD/HD) is a neurological disorder that affects mainly children and teenagers. Some stats indicate that 5.29% of kids under 19 suffer this disorder. The children affected with it usually suffer difficulties to pay attention, to stay focused in a single activity, and might be excessively active. Often, this leads to issues at the school, or following simple commands. Some studies have identified that mobile applications could help in the treatment of children with AD/HD, by contributing to improve different types of attention. We propose Dividi2, a mobile application that aims to improve the split attention in kids with AD/HD. We use some concepts from the recommendations obtained in the EMOINAD guide, related to patterns for the creation of mobile interfaces for children with AD/HD. We also conduct a little experiment with 5 children and their parents in order to validate our prototype. The feedback obtained was very positive, with 100% of the children indicating they would play Dividi2 and 80% indicating they would play it with their friends.
Intrusion Detection Algorithm Based on Deep Learning for Industrial Control Networks

In this paper, we describe a intrusion detection algorithm based on deep learning for industrial control networks, aiming at the security problem of industrial control network. Deep learning is a kind of intelligent algorithm and has the ability of automatically learning. It use self-learning to enhance the experience and dynamic classification capabilities. The ideology of deep learning is similar to the idea of intrusion detection to improve the detection rate and reduce the rate of false through learning, a sparse auto-encoder-extreme learning machine intrusion detection model is proposed for the problem of intrusion detection accuracy. It uses deep learning autoencoder to combine the coefficient penalty and reconstruction loss of the encode layer to extract the features of high-dimensional data during the training model, and then uses the extreme learning machine to quickly and effectively classify the extracted features. The accuracy of the algorithm is verified by the industrial control intrusion detection standard data set. The experimental results verify that the method can effectively improve the performance of the intrusion detection system and reduce the false alarm rate.
Brain Tumor Screening using Adaptive Gamma Correction and Deep Learning

Generally, the brain tumor is regarded as one of the most dangerous diseases. It is always too late to detect the brain tumors, as the tumors at the early stage are always ignored. In fact, the traditional manual diagnosis process is inefficient. The radiologists have to accomplish a great amount of reading work per day, which can result in weariness and thus lead to misdiagnosis. To liberate radiologists from endless work, a brain tumor screening system based on adaptive gamma correction and deep learning is proposed. The brain images are labeled with "non-tumor" and "tumors", and the radiologists just needs to deal with the brain images labeled with "tumors", which can significantly reduce the workload of the radiologists. Firstly, sufficient contrast enhanced T1-weighted brain images are collected. Further, background removal based on iterative threshold and a novel adaptive gamma correction (NAGC) are implemented to generate brain images with similar overall intensity. Finally, data augmentation technologies are applied to enlarge the training set, and convolutional neural network (CNN) is adopted to train the classifier. The results indicate that the accuracy of the proposed system can reach 95.13%.
A Survey of Context Simulation for Testing Mobile Context-Aware Applications

Equipped with an abundance of small-scale microelectromechanical sensors, modern mobile devices such as smartphones and smartwatches can now offer context-aware services to users in mobile environments. Although advances in mobile context-aware applications have made our everyday environments increasingly intelligent, these applications are prone to bugs that are highly difficult to reproduce and repair. Compared to conventional computer software, mobile context-aware applications often have more complex structures to process a wide variety of dynamic context data in specific scenarios. Accordingly, researchers have proposed diverse context simulation techniques to enable low-cost and effective tests instead of conducting costly and time-consuming real-world experiments. This article aims to give a comprehensive overview of the state-of-the-art context simulation methods for testing mobile context-aware applications. In particular, this article highlights the technical distinctions and commonalities in previous research conducted across multiple disciplines, particularly at the intersection of software testing, ubiquitous computing, and mobile computing. This article also discusses how each method can be implemented and deployed by testing tool developers and mobile application testers. Finally, this article identifies several unexplored issues and directions for further advancements in this field.
A Syndicated Adaptive News-feed: The Energy Hub

Adaptive User Interfaces (AUI) change their presentation and content depending on the individual needs of their client. In our work, we aim to create a AUI that collects articles on “energy-related” issues from various publishers using Google’s Custom Search and NEWS API. We call this AUI news-feed service “The Energy Hub”. We have compared the articles that result from a search from generic search engines with that of “Energy Hub”. The latter shows more promising results with better precision. In future work, we aim to provide all thematically related articles to the reader, customised by user preferences and by their interactions with the AUI. The intention is to improve the reader’s experience using text mining and search-term clustering, including topic modelling. An important aspect of our work is the judgement of the success of the Energy-hub AUI, and we identify a methodology for its evaluation.
Modalities of Expression: Capturing Embodied Knowledge in Cooking

When cooking we negotiate between instructions in recipes and personal preferences to make in-the-moment creative decisions. This process represents moments of creativity that utilise and reveal our embodied knowledge. This paper focuses on the capture of expressions of embodied knowledge by digitally-networked utensils. We present a design process investigating the design of tangible interfaces to capture and communicate embodied knowledge as a proposition for recipe authoring tools for open innovation in food. We reflect upon this process to discuss lessons about the individual nature of embodied knowledge and its expression, and the context of capturing it to make design recommendations.
A principled formulation of integrating objects in Monocular SLAM

Monocular SLAM is a well-studied problem and has shown significant progress in recent years, but still, challenges remain in creating a rich semantic description of the scene. Feature-based visual SLAMs are vulnerable to erroneous pose estimates due to insufficient tracking of mapped points or motion induced errors such as in large or in-place rotations. We present a new SLAM framework in which we use monocular edge based SLAM [1], along with category level models, to localize objects in the scene as well as improve the camera trajectory. In monocular SLAM systems, the camera track tends to break in conditions with abrupt motion which leads to reduction in the number of 2D point correspondences. In order to tackle this problem, we propose the first most principled formulation of its kind which integrates object category models in the core SLAM back-end to jointly optimize for the camera trajectory, object poses along with its shape and 3D structure. We show that our joint optimization is able to recover a better camera trajectory in such cases, as compared to Edge SLAM. Moreover, this method gives a better visualization incorporating object representations in the scene along with the 3D structure of the base SLAM system, which makes it useful for augmented reality (AR) applications.
Enriched DNA Strands Classification using CGR Images and Convolutional Neural Network

Bioinformatics is the biological study which applies programming techniques for more understanding and analysis of living objects such as the study of genome structure. The genome structure could be represented in the form of an image. Chaos Game Representation (CGR) is the practice of converting the DNA sequence (i.e., genomes) into images, where each image is a graphical appearance for an individual DNA strand's signature. CGR is a method of converting a long one-dimensional DNA sequence into a graphical form. This method provides a visual image of a DNA sequence different from the traditional manual linear arrangement of nucleotides polymerase chain reaction. In the recent years, CGR was introduced to automatically classify genomes not only by archival references but also through its' unique signature. In this paper, a novel CGR classification approach is developed combining the advances of image processing and pattern recognition approaches. The approach starts by declaring the genome and using the CGR technique to map it to the graphical interface (i.e., 16x16 signature images). Then, an image processing procedure is prepared to handle complex geometric shapes, analyze structured and visualized genome sequences and fractal point the included nucleotides of these images. Finally, the convolutional neural network was designed and well-trained by those signatures to classify each genome tested.
Towards projectional editing for model-based SPLs

Model-based software product lines (MBSPLs) are implemented using various variability mechanisms. These are commonly categorized whether they separate features virtually (e.g., using annotations) or physically (e.g., using modules). Each of these mechanisms comprises advantages and disadvantages regarding MBSPL development, maintenance and analysis. To date, MBSPL developers have to choose upfront which variability mechanism to use, and the chosen mechanism including its drawbacks is bound to the MBSPL's entire lifecycle. In contrast, projectional editing has recently shown very promising potential of making the development of classical SPLs (e.g., implemented in C/C++) more flexible. User-editable projections allow developers to switch fluidly between different variability mechanisms based upon a common internal representation known as variational abstract syntax tree. In this paper, we report on ongoing work on the projectional editing of MBSPLs, which is challenged by a set of additional requirements. We lay the foundation for different editable projections using a common variational abstract syntax graph (vASG) as internal representation. This vASG is used for a fine-grained variability representation of EMOF-based models. We demonstrate the feasibility of our approach by incorporating different variability mechanism projections (150% models and delta modules) and modeling languages (Ecore class diagrams and UML state machines) used in existing MBSPL case studies.
Data-Driven Modelling of Daylight Scattering by Roman Window Glass

With the advent of window glass, its optical properties arise as one important factor in the illumination, and thereby the visual perception, of Roman architecture. Computational simulation allows to reconstruct the daylight illumination of buildings with known geometrical configuration if the scattering properties of window glass can be replicated. We describe a method to generate data-driven models of Roman window glass based on precise gonio-photometric measurements of archaeological finds. The method is employed to model four exemplary glass finds in the context of a simplified architectural scene, demonstrating the potential to support research in building history and archaeology focused on building techniques, lighting concepts, and perception with accurate and reliable photometric data.
Epileptic Seizure Detection Using Convolutional Neural Network: A Multi-Biosignal study

Epilepsy affects over 70 million people worldwide, making it one of the most common serious neurological disorders in the world. The automated identification of seizures based on EEG signal is one of the most common methods but facing challenges such as the variability of seizures between individual patients and artifact generated during the measurement. In this work, we implement the multi-biosignals scheme for seizure detection by combing EEG, ECG and respiratory. We apply 1D and 2D convolutional neural network (CNN) on multi-biosignal epileptic seizure detection using the in-situ dataset with artifacts. The experimental results show that incorporating multi-biosignals outperforms than using EEG only. We also discovered that Conv2D model could achieve the best AUC of 65%, which is 7% better than the Conv1D model.
Uniformly Interpolated Balancing for Robust Prediction in Translation Quality Estimation

There has been growing interest among researchers in quality estimation (QE), which attempts to automatically predict the quality of machine translation (MT) outputs. Most existing works on QE are based on supervised approaches using quality-annotated training data. However, QE training data quality scores readily become imbalanced or skewed: QE data are mostly composed of high translation quality sentence pairs but the data lack low translation quality sentence pairs. The use of imbalanced data with an induced quality estimator tends to produce biased translation quality scores with “high” translation quality scores assigned even to poorly translated sentences. To address the data imbalance, this article proposes a simple, efficient procedure called uniformly interpolated balancing to construct more balanced QE training data by inserting greater uniformness to training data. The proposed uniformly interpolated balancing procedure is based on the preparation of two different types of manually annotated QE data: (1) default skewed data and (2) near-uniform data. First, we obtain default skewed data in a naive manner without considering the imbalance by manually annotating qualities on MT outputs. Second, we obtain near-uniform data in a selective manner by manually annotating a subset only, which is selected from the automatically quality-estimated sentence pairs. Finally, we create uniformly interpolated balanced data by combining these two types of data, where one half originates from the default skewed data and the other half originates from the near-uniform data. We expect that uniformly interpolated balancing reflects the intrinsic skewness of the true quality distribution and manages the imbalance problem. Experimental results on an English-Korean quality estimation task show that the proposed uniformly interpolated balancing leads to robustness on both skewed and uniformly distributed quality test sets when compared to the test sets of other non-balanced datasets.
The Practice Research of Flipped-Classroom Combined Mobile Action Learning in International Marketing Management Course

Based on the seven-year training program for Taiwan Science Industrial Parks, this project aims to subsidize students' double-qualification and undergraduate guidance as well as internships in the industry. However, the effectiveness of classroom teaching before internship is not satisfactory. The courses are all based on one-way teaching Students' learning motivation is low. Unfortunately, the teaching methods cannot be broken despite the complete program arrangement. Through this action research project to implement the flip classroom combined with blended action learning, this training program can complete each course of innovation teaching and create innovative teaching materials to enhance student motivation, the researchers will be in the study constantly adjust and try to finally achieve the effectiveness of international logistics personnel training so that students can develop independent thinking and thinking ability before graduation. The final results show that the hybrid action teaching allows students to solve the downloading of videos, discussions and opinions on the mobile phone through the PBL problem-oriented use of Happy Teaching 3.0. The industrial lecturers provide case studies and final license exams to enable students to improve their employability in the workplace. Finally, from the final student feedback, course participation, and license pass rate, it can be proved that mixed action teaching is helpful and positive for students.