Reviewerly/exHarmony huggingface dataset:
Use of RNN in Devangari Script

In India, Devanagari script is used most prevalently. But recognizing the handwritten documents of Devanagari Script is a challenging task due to variations in writing, rendering, and cursive nature of handwriting. However, there has been a lot of improvement in research in the field of recognizing Handwritten Documents. In this, we will implement a framework that inputted the number of images gives an output for recognized characters.
Using formal ontology for the representation of morphological properties of anatomical structures in endoscopic surgery

Abstract In this paper we present results to the problem of an adequate and compact symbolic representation of morphological features of anatomical structures that serve as surgical landmarks for automated assistance in endoscopic surgery using the General Formal Ontology (GFO) as a formal framework. For this purpose, we employed a translation from this first-order logic representation to a more compact description logic based formalism with the associated benefits, such as the availability of decidable reasoning procedures, for the purpose of automated landmark recognition in a hybrid symbolic/subsymbolic AI approach.
Software startups in growth phase SE practices adopted to SEE

Context: Software has become ubiquitous in every corner of modern societies. During the last five decades, software engineering has also changed significantly to advance the development of various types and scales of software products. In this context, Software Engineering Education plays an essential role in keeping students updated with software technologies, processes, and practices that are popular in industries. Aim: In this PhD work, I want to answer the following research questions: To what extent software engineering trends are present in software engineering education? In which way software startup in growth phase characteristics can be transferred into software engineering education context? What is the impact of software startup engineering in the curriculum and to software engineering students? Method: I utilize literature review and mix-methods approaches (quantitative and qualitative data and methods triangulation) in gathering empirical evidence. More precisely, I split my research method into two phases. The first phase of the research will acquire knowledge and insight based on the existing literature review. The second research phase will split the focus in two directions. Firstly, I shall gather empirical evidence on how software startup practices are present in software engineering education. Secondly, I will conduct parallel investigations into SE practices in growth phase software startups. Expected Results: I argue that software startup engineering practices are an ultimate tool for software engineering education approaches. I expect students to acquire software engineering skills in a more realistic context while using software startup in growth phase practices.
EARTHQUAKE INFORMATION PUSH NOTIFICATION SYSTEM IN ANDROID APPLICATION USING GOOGLE FIREBASE

Indonesia is a country located between three tectonic plates, namely Eurasia, the Pacific and Indo-Australia, which is one of the active plates, so that there is the potential for earthquakes, which are sometimes followed by tsunami disasters. The latest accurate, precise, and trusted earthquake information is needed by all parties. For this reason, a system is needed to inform the latest earthquake so that the impact can be minimized. Departing from open data published by BMKG, which is an earthquake in real time, a push notification system for earthquake information was designed to the Android application using Google Firebase. Push notifications are adjusted for specific areas based on user wishes. When the user is in the vicinity of an earthquake-affected or surrounding area, or the location of an earthquake is in a city that is monitored by the user, the notifications that appear can be alerts. Whereas when the user is in a location that is not affected by an earthquake, the notification that appears is only as information that there is an earthquake in a certain area. After analyzing, designing, implementing and testing using the black box testing method, push notification system functionally produces results that are as expected.
The Effect of Talking Chips Towards Students’ Self-Assurance in Speaking Skill

This research is aimed to find out whether Talking Chips has positive effect or not toward students’ self-assurance in speaking of the second grade students at SMAN 4 Mataram. The research was experimental research and the design used was Pre Experimental with one group without control group and used one group pre-test post-test design only. The population of the study was the second grade students of SMAN 4 Mataram which consisted of one class (19 students). One class was chosen by using accidental sampling technique and the result was XI IPS 1. In collecting the data pre-test and post-test were done. And then, the data analyzed by using SPSS. The result of data analysis showed that t-test was 3.457 and t-table was 2.109, it means that t-test was higher than t-table as the level of significance level 0,05. It can be concluded that teaching self-assurance in speaking skill at SMAN 4 Mataram using talking chips produce much greater change in the students’ self-assurance in speaking. On the other words, talking chips technique is effective in teaching self-assurance in speaking at SMAN 4 Mataram.
Exploring the Issues of Technology Based Art Education for Future Education

테크놀로지, 뉴미디어 리터러시, 사고력과 문제해결력을 미술교육에 접목시키려는 시도는 미래 미술교육의 모습을 구체화하기 위한 출발이 될 수 있다. 본 논문에서는 테크놀로지 기반 미술교육의 다양한 쟁점들을 조망하고, 실천 가능한 방법들을 탐구하고자 한다. 이에 미술교육 현장에서 도입이 진행되고 있거나 예정에 있는 기기들의 검토 과정을 교사의 실습과성찰을 통해 자료 분석하여 제시하였다. 이를 위해 먼저 미래 미술교육 구성을 위한 전제로 BYOD와 에듀테크의 가능성을논의하였다. 다음으로 테크놀로지 기반 미술교육을 가능케 하는 첨단 매체들의 특성과 현장에의 적용 가능성 등을 분석함으로써 미래 미술교육에서 활용할 수 있는 토대를 탐색하였다. 타블렛을 활용한 디지털 드로잉, 레이저 커팅기를 활용한메이커 교육, 3D프린터를 활용한 입체 창작교육의 실제와 가능성을 탐구하고, 유의미한 교육으로 발전시키기 위한 고려사항을 논의하였다.Currently called the period of the 4th Industrial Revolution, we need to explore the future of art education based on the intrinsic goals and values of art education as well as the educational philosophies and learners’ competencies demanded by society in the future. This study aims to look at various issues of technology-based art education and to explore practical methods in the real setting. TTo construct the future art education, this article first explored the issues of BYOD and Edutech. Next, it explored the realities and possibilities of digital drawing using a tablet, maker education using a laser cutting machine, and stereoscopic creative education using a 3D printer. Further, this article discussed educational considerations to develop this practice and activity using high-tech media into meaningful education.
Performance Analysis Study of Stochastic Computing Based Neuron

Deep learning systems are computationally more expensive and their performance depends strongly on the underlying hardware platform. The basic requirements of a deep neural network (DNN) are simple and high-precision computations that can be performed faster. A deep neural network involves enormous data manipulations, of which basic arithmetic operations such as addition and multiplication form the building blocks. But implementing these operations in DNN is a challenging task with less hardware requirements. In this paper, a study has been made on how DNN effectively processes the diverse multi-sensor data. All these can be taken into consideration for the applications which work on constrained resource environment, e.g., IoT devices.
Design and Implementation of a Pre-Surgical Investigation System by VR+AR+MR Techniques

Virtual-Reality (VR), Augmented-Reality (AR) and Mixed-Reality (MR) are finding new applications in clinical investigations. This study uses VR, AR, and MR techniques for pre-surgery applications on three platforms: Windows PC, Apple iPad, and Mobile. Using the proposed technique, which involves the use of virtual reality, augmented reality, and mixed reality technologies, a physician can visualize the problem and discuss options with a patient while the patient's anatomy is being studied. The combination of endoscopic visualization can be used for surgical planning, training, medical diagnosis, and postoperative examination.
A study on the Application of the Infant Forest·Art Play Program Focused on creative art expression

본 연구는 만 2세 영아가 숲·예술놀이 프로그램에서 어떠한 예술적 경험을 하는지 탐색하고 그 의미를 분석해 보고자 하였다. 숲·예술놀이 프로그램은 문헌고찰, 요구분석, 초안 구성, 1차 전문가 협의, 예비연구, 2차 전문가 협의를 거쳐 도입(감각열기-예술적 요소 탐색하기), 전개(개념인식-예술적 요소 표현하기), 마무리(감상 및 토의하기)로 구성하였다. 연구참여자는G시에 소재한 P 어린이집 만 2세 영아 11명(남아 6명, 여아 5명)으로 평균 월령은 30개월이다. 자료수집은 2018년 8월29일부터 11월 30일까지 주 2회(회기별 1시간 30분)로 총 24회 이루어졌으며, 참여 관찰, 교사와의 면담, 연구자의 현장일지와 동영상 자료와 그리고 사진 자료를 질적 분석하였다. 연구결과, 숲·예술놀이 프로그램에 참여한 영아는 숲에서 느낀아름다움과 자연물을 매개로 하여 미술, 음악, 동작, 가작화 놀이로 창의적인 예술 표현을 하는 것으로 나타났다. 생명체가공존하고 항상 변화하는 열린 공간인 숲에서 영아는 민감해진 감각을 이용하여 자발적으로 예술적 요소를 탐색하고, 창의적인 예술적 표현을 하였다. 영아의 발달적 특성과 영아의 표현 욕구를 충족시켜주는 숲·예술놀이 프로그램은 숲이 영아에게예술놀이를 하기에 의미 있는 장소라는 점과 이 프로그램이 영아의 창의적 역량을 키워 궁극적으로 영아의 행복한 삶을영위하게 한다는 것을 드러냈다는 데에 의의가 있다.This study was to explore the artistic experiences of 2-year-old toddlers in the Forest·Art Play program and analyze their meanings. The program consists of introduction(sensory opening-discovery of artistic elements), development(concept recognition-expression of artistic elements), and finishing(appreciation and discussion). Participants in the study include eleven 2-year-old infants(six boys and five girls) at the P Daycare Center in G city. The date collection was conducted 24 times with participation observation, interviews with teachers, field diaries, video materials, and photographic data. Qualitative analysis was performed. The study found that infants in the Forest·Art Play program perform creative art expressions with art, music, movements, and pretend play, using the beauty and natural objects felt in the forest. In the forest, infants voluntarily explored artistic elements and creative artistic expression using their sensitive senses. The Forest·Art Play programs is meaningful that the forest is a meaningful place for infants to do Art Play and that the program improves the creative capacity of infants and ultimately leads to a happy life infants.
V-DOOR

In recent years and with its accessibility, the use of online shopping for clothing has increased. Virtual Dressing Rooms (VDRs) represent an effective way to enact the ability to before buying, thus removing an important obstacle for online shopping. While most of the VDR tools that have been realized so far are based on Augmented Reality and are installed directly inside the retail shops, this paper proposes a real-time VDR application titled V-DOOR that leverages the features of Oculus Rift to create an immersive experience that enables customers to try on clothes virtually in the comfort of their own home rather than physically in the retail shop.
Design and evaluation of a blended mental health curriculum for undergraduate medical education in Pakistan

There is a significant shortage of qualified psychiatrists and related service providers in Pakistan; this occurs in the backdrop of burgeoning evidence of a high prevalence of mental health disorders in the country. At the Aga Khan University Medical College, Psychiatry has been taught to the undergraduates as a mandatory one-month clinical rotation since 1987. AKU is the only medical college with a formal psychiatry training at the undergraduate level. In 2017, a team of faculty members reassessed the curriculum, teaching methodology and learning outcomes of the psychiatry rotation. As a result, an outcome-based, blended curriculum was devised that integrated virtual and classroom-based learning with patient skills training. The new curriculum has now been in implementation for one year. At the end of the rotation, a questionnaire was administered to determine students’ experience of online learning. WEBLEI was adapted to measure students’ perceptions across four subscales. Data was gathered from learners’ use of the virtual learning system, participation in facilitator-based sessions and the clinical teaching and learning. Students were also asked to compare didactic face-to-face sessions with blended learning. Blended learning scored higher on all measures such as student engagement, critical thinking, student-led learning and alignment of assessment method with learning outcomes. The analysis of this data has informed future directions regarding the design and implementation of a Pakistan-wide mental health program. This study also highlights the challenges of curriculum design and its implications in the broader context of Pakistan. The results add to the existing literature about the experience, challenges and successful outcomes of designing a blended curriculum for Pakistan.&#x0D; Keywords: Blended learning, Integrated curriculum, Mental health, Learning analytics, PakistanHow to cite this article:Chachar, A.S., Siddiqui, S.A., Saeed, H., Naseem, A. &amp; Mian, A.I. 2020. Design and evaluation of a blended mental health curriculum for undergraduate medical education in Pakistan. Scholarship of Teaching and Learning in the South. 4(2): 100-117. https://doi.org/10.36615/sotls.v4i2.111.This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
Ivan Racetin, PhD in Technical Sciences

On 19 June 2020, Ivan Racetin defended his doctoral thesis Anomaly detection on hyperspectral and multispectral digital images of a mine suspected area at the Faculty of Geodesy, University of Zagreb.
Weakly Supervised Leaming of Discriminative Features for Fine-Grained Visual Categorization

Fine-Grained Visual Categorization (FGVC) is one of challenging domains in computer vision due to the subtle differences between the subordinate categories which belong to one basic category. In order to solve this problem, recent FGVC methods could localize the discriminative regions firstly and then extract fine-grained features from these regions. However, it is difficult to mine consistent and adequate discriminative regions for final recognition. In this paper, a novel framework is proposed for FGVC which is composed of three branches which extract global features, discriminative fine-grained features and complementary fine-grained features, respectively. Concretely, discriminative fine-grained features are extracted through region from class activation map (CAM) method and complementary fine-grained features are extracted through regions from feature pyramid networks (FPN) assisted with algorithm to learn the rank of regions. Experimental results have shown that the proposed method achieve better performance than several state-of-the-art models on three datasets, such as CUB-200-2011, Stanford Cars and FGVC Aircraft.
A Case Study of Community-Based Participatory Learning Program for Family in Museum

오늘날은 사회 전반에 걸쳐 대중들간의 활발한 소통, 공감, 교류, 참여가 강조되는 시대이다. 이러한 시대적 특징에 따라박물관에서도 ‘참여’와 ‘지역사회’라는 두 키워드에 중점을 두면서 박물관의 벽을 넘어 지역사회와의 활발한 상호작용을 지향하고 있다. 이에 본 연구는 지역사회와 박물관의 긴밀한 연계를 통한 사회적 참여를 도모할 수 있는 방안으로서 ‘지역기반참여적 박물관’을 상정하였다. 이를 위해 우선적으로 지역기반 참여적 학습 프로그램의 이론적 배경이 되는 세 가지 이론, 곧, 참여적 박물관, 참여적 학습, 지역기반학습에 대한 소개를 하였으며, 이들 이론들을 종합하여 ‘지역기반 참여적 학습’이라는 새로운 학습방법을 도출하였다. 이후 이에 따라 대구 디아크 문화관이라는 지역기반 박물관을 선정하여, 그 지역에 사는가족을 대상으로 하여 지역기반 참여적 학습 프로그램을 개발, 적용하였다. 참여자들의 지역사회에 대한 소속감과 관심에있어서의 변화를 확인하기 위해, 다양한 질적 자료를 수집, 분석하여, 그 결과를 분석하였다. 결론적으로 본 연구는 ‘지역기반참여적 학습방법’이 지역사회라는 공동체적 맥락 속에서 박물관의 새로운 역할에 대한 구체적 방안의 하나가 될 수 있음을제안하고자 했다.Recently active interaction and participation among the public has been more distinctively noticed than ever before. According to these social trends, the museum also has focused on two key words, ‘participation and community , aiming for connection with the community. In this context, this study proposed a “Community-Based Participatory Museum” as a way to promote social participation of museum through close relationships between the community and the museum. For this purpose, this study, first, presented three theories as its theoretical background: Participatory museum, participatory learning, and community-based learning, which was followed by the introduction of a new learning method called ‘Community-Based Participatory Learning Approach’. Based upon the learning method, a case study of a family-participating museum education has been conducted at The ARC (The architecture of River Culture) as one of community-based museum in Daegu. Diverse qualitative data were collected and analyzed to examine any changes of identity and interests of the participants in stance fo the community members. In conclusion, this study intended to suggest that the community-based participatory learning approach could be a concrete way to practice the social role of the museum in the context of the community-connected museum.
UCAI 2020: Workshop on User-Centered Artificial Intelligence

UCAI'20: - Workshop on User-Centered Artificial Intelligence, Magdeburg, Germany, 9th September 2020
Opening the software engineering toolbox for the assessment of trustworthy AI.

Trustworthiness is a central requirement for the acceptance and success of human-centered artificial intelligence (AI). To deem an AI system as trustworthy, it is crucial to assess its behaviour and characteristics against a gold standard of Trustworthy AI, consisting of guidelines, requirements, or only expectations. While AI systems are highly complex, their implementations are still based on software. The software engineering community has a long-established toolbox for the assessment of software systems, especially in the context of software testing. In this paper, we argue for the application of software engineering and testing practices for the assessment of trustworthy AI. We make the connection between the seven key requirements as defined by the European Commission's AI high-level expert group and established procedures from software engineering and raise questions for future work.
Tailoring r-index for Document Listing Towards Metagenomics Applications

A basic problem in metagenomics is to assign a sequenced read to the correct species in the reference collection. In typical applications in genomic epidemiology and viral metagenomics the reference collection consists of a set of species with each species represented by its highly similar strains. It has been recently shown that accurate read assignment can be achieved with k-mer hashing-based pseudoalignment: a read is assigned to species A if each of its k-mer hits to a reference collection is located only on strains of A. We study the underlying primitives required in pseudoalignment and related tasks. We propose three space-efficient solutions building upon the document listing with frequencies problem. All the solutions use an r-index (Gagie et al., SODA 2018) as an underlying index structure for the text obtained as concatenation of the set of species, as well as for each species. Given t species whose concatenation length is n, and whose Burrows-Wheeler transform contains r runs, our first solution, based on a grammar-compressed document array with precomputed queries at non terminal symbols, reports the frequencies for the \(\texttt {ndoc}\) distinct documents in which the pattern of length m occurs in \(\mathcal{O}(m + \log (n)\texttt {ndoc}) \) time. Our second solution is also based on a grammar-compressed document array, but enhanced with bitvectors and reports the frequencies in \(\mathcal{O}(m + ((t/w)\log n + \log (n/r))\texttt {ndoc})\) time, over a machine with wordsize w. Our third solution, based on the interleaved LCP array, answers the same query in \(\mathcal{O}(m + \log (n/r)\texttt {ndoc})\) time. We implemented our solutions and tested them on real-world and synthetic datasets. The results show that all the solutions are fast on highly-repetitive data, and the size overhead introduced by the indexes are comparable with the size of the r-index.
Joint Demosaicking / Rectification Of Fisheye Camera Images Using Multi-Color Graph Laplacian Regularization

To compose a 360° image from a rig with multiple fisheye cameras, a conventional processing pipeline first performs demosaicking on each fisheye camera’s Bayer-patterned grid, then translates demosaicked pixels from the camera grid to a rectified image grid—thus performing two image interpolation steps in sequence. Hence interpolation errors can accumulate, and acquisition noise in the captured pixels can pollute neighbors in two consecutive processing stages. In this paper, we propose a joint processing framework that performs demosaicking and grid-to-grid mapping simultaneously—thus limiting noise pollution to one interpolation. Specifically, we first obtain a reverse mapping function from a regular on-grid location in the rectified image to an irregular off-grid location in the camera’s Bayer-patterned image. For each pair of adjacent pixels in the rectified grid, we estimate its gradient using the pair’s neighboring pixel gradients in three colors in the Bayer-patterned grid. We construct a similarity graph based on the estimated gradients, and interpolate pixels in the rectified grid directly via graph Laplacian regularization (GLR). Experiments show that our joint method outperforms several competing local methods that execute demosaicking and rectification in sequence, by up to 0.52 dB in PSNR and 0.086 in SSIM on the publicly available dataset, and by up to 5.53dB in PSNR and 0.411 in SSIM on the in-house constructed dataset.
Multi-model coupling method for imbalanced network traffic classification based on clustering

The imbalanced category of network traffic poses a challenge to the classification methods based on machine learning, because the unbalanced data structure affects the performance of machine learning algorithms. In this paper, we propose a multi-model coupling approach to address the imbalanced data problem in network traffic classification. We process the major class to some clusters by a clustering algorithm. Then, these clusters and the minor class are used to form the training dataset for training model respectively. During the test, the test dataset is input into the previously trained models respectively, and the classification results of respective models are coupled to obtain the final result. We tested our proposed method on two well-known network traffic datasets and the results showed that it could achieve better performance and less time consumption compared with recent proposed methods in the case where the ratio of minor to major classes is very small.
Distortion estimates for approximate Bayesian inference

Current literature on posterior approximation for Bayesian inference offers many alternative methods. Does our chosen approximation scheme work well on the observed data? The best existing generic diagnostic tools treating this kind of question by looking at performance averaged over data space, or otherwise lack diagnostic detail. However, if the approximation is bad for most data, but good at the observed data, then we may discard a useful approximation. We give graphical diagnostics for posterior approximation at the observed data. We estimate a distortion map that acts on univariate marginals of the approximate posterior to move them closer to the exact posterior, without recourse to the exact posterior.