Reviewerly/exHarmony huggingface dataset:
Building Dataverse Communities that follow RDA Best Practices for Data Sharing and Management

Presentation at the RDA 14th Plenary conference, arranged by Research Data Alliance, Helsingfors, 23.10.19 - 25.10.19. https://www.rd-alliance.org/plenaries/rdas-14th-plenary-helsinki-finland.
Chapter 10. An Ontology Design Pattern for Representing Recurrent Situations

In this paper, we present an Ontology Design Pattern for representing situations that recur at regular periods and share some invariant factors, which unify them conceptually: we refer to this set of recurring situations as recurrent situation series. The proposed pattern appears to be foundational, since it can be generalised for modelling the top-level domain-independent concept of recurrence, which is strictly associated with invariance. The pattern reuses other foundational patterns such as Collection, Description and Situation, Classification, Sequence. Indeed, a recurrent situation series is formalised as both a collection of situations occurring regularly over time and unified according to some properties that are common to all the members, and a situation itself, which provides a relational context to its members that satisfy a reference description. Besides including some exemplifying instances of this pattern, we show how it has been implemented and specialised to model recurrent cultural events and ceremonies in ArCo, the Knowledge Graph of Italian cultural heritage.
Toward Co-Creative Tools for Tactical Urban Revitalization

The physical urban environment has been shown to play a large role in community resilience and well-being, but processes to plan for, revitalize, and repair these environments often show the goals of local residents to be at odds with official agendas. My research focuses on building co-creative design aids for ordinary citizens to help them create expert-level visualizations to communicate plans for tactical, urban revitalization projects in their communities. I rely on a variety of both technical and qualitative methods to build tools and algorithmic techniques as well as understand the scope of design knowledge of novice community members.
Subject Guides: LabArchives: Frequently Asked Questions

LabArchives is an innovative cloud-based ELN (electronic lab notebook) to enable researchers to store, organize, and publish their research data.
El video didáctico en el proceso de enseñanza de la Matemática en el nivel secundaria

Se presenta un analisis teorico-conceptual con relacion al uso de tecnologia educativa y su integracion en el proceso de ensenanza-aprendizaje en educacion secundaria, con la intencion de estructurar un marco referencial que permita respaldar un proyecto de intervencion y mejora de la clase de matematicas en el contexto de la ensenanza remota de emergencia ante la situacion de emergencia sanitaria provocada por el Covid-19. El proposito del proyecto es la innovacion pedagogica mediante el diseno de videos didacticos para la ensenanza de la matematica en el nivel secundaria. Para el marco teorico se aborda la teoria del conectivismo desarrollada por George Siemens y para el marco conceptual se desarrolla la nocion de video didactico y su variante como video interactivo, asi como lo referido al aula invertida como modelo pedagogico. Tambien se analizan estudios acerca del video didactico como herramienta de ensenanza aprendizaje y como un medio importante en la metodologia del aula invertida. Se concluye con una reflexion acerca de los retos que los docentes afrontan y como el proyecto se ajusta a las necesidades actuales de un contexto particular. Palabras clave: Video didactico, aula invertida, innovacion, matematicas, nivel secundaria. ABSTRACT A theoretical-conceptual analysis is presented in relation to the use of educational technology and its integration in the teaching-learning process in secondary education, with the intention of structuring a referential framework that allows to support an intervention project and improvement of the mathematics class in the context of emergency remote teaching in the face of the health emergency situation caused by Covid-19. The purpose of the project is pedagogical innovation through the design of didactic videos for the teaching of mathematics at the secondary level. For the theoretical framework, the theory of connectivism developed by George Siemens is approached and for the conceptual framework the notion of didactic video and its variant as interactive video is developed, as well as that referred to the flipped classroom as a pedagogical model. Studies about the didactic video as a teaching-learning tool and as an important means in the flipped classroom methodology are also analyzed. It concludes with a reflection on the challenges that teachers face and how the project adjusts to the current needs of a particular context. Keywords: Didactic video, flipped classroom, innovation, mathematics, secondary level.
Measuring Maintainability of Object Oriented Design: A Revisit

Maintainability has always been an elusive concept. Software maintainability is an external software quality attributes that estimate the complexity and effort required for maintaining software. The key concern of this review paper will be organized study on maintainability considering in view by its sub factors and metrics implementation of software maintainability keeping in mind to supports the maintenance process and facilitates the formation of improved quality software. In this paper studies accomplish a systematic literature review to have widespread facts of maintainability research and its feature factors and related measurements. Finally, study does a comparative analysis on software maintainability models developed by various researchers/area experts including their contribution and limitation. In last our effort is to find the known wide-ranging and complete model or framework for quantifying the maintainability of object-oriented software at an initial stage of the development life cycle.
Semi-supervised Person Re-identification by Attribute Similarity Guidance

Although supervised person re-identification (RE-ID) has achieved great progress with deep learning, it requires time-consuming annotation of a large number of pedestrian identities. To reduce labeling cost, we attempt to reduce cross-camera identity annotations and exploit pedestrian attribute annotations as auxiliary information instead. The pedestrian attributes, such as outfit styles, contain coarse semantic knowledge. Although pedestrian attributes are annotated without exhaustive searching in a camera network, which is much easier than cross-camera identity annotation, ambiguity exists in attributes when different persons have similar outfits. To solve this problem, we propose an Attribute Similarity Guidance loss (ASG) to guide appearance feature learning for RE-ID by selective attribute similarity preservation to avoid the impact of such ambiguity. Finally, we develop an attribute-guided self training framework to jointly utilize attribute annotations, unlabeled data and limited labeled data for semi-supervised learning. Extensive experiments on Market-1501 and DukeMTMC-ReID show the superiority of our method for semi-supervised RE-ID.
Where HCI Meets the Spiritual Path: The Three Yogas of the Bhagavad Gītā

HCI practice can be a force for social good, but can it become a spiritual practice? If so, how? These questions are somewhat taboo, and usually discussed quietly at the fringes of the HCI community. This paper is based on the Bhagavad-Gita, and proposes three ways to tie-together a practical HCI career with our spiritual lives. The three approaches are broadly related to humanitarian action (karma yoga), love and devotion (bhakti yoga) and introspective insight (jnana yoga). Each offers a different perspective on our HCI practice, and how the practical challenge of being a researcher can be reframed as part of a spiritual path. It suggests approaches to issues such as emotional burnout and bias-awareness. It is based on teachings given by major Indian teachers.
Comparison of local kinematic rupture joint inversion approaches for tsunami early warning: Examples of the 2017-2020 Mw &gt; 6.3 East Aegean earthquakes

&amp;lt;p&amp;gt;The fast inversion of reliable centroid moment tensor and kinematic rupture parameters of earthquakes occurring near coastal margins is a key for the assessment of the tsunamigenic potential and early tsunami warning (TEW). In recent years, more and more multi-channel seismic and geodetic online station networks have been built-up to improve the TEW, for instance the GNSS and strong motion networks in Italy, Greece, and Turkey, additionally to the broadband seismological monitoring. Inclusion of such data for the fast kinematic source inversion can improve the resolution and robustness of its&amp;amp;#8217; solutions. However, methods have to be further developed and tested to fully exploit the potential of such rich joint dataset.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this frame, we compare and test two in-house developed, kinematic / dynamic rupture inversion methods which are based on completely different approaches. The IDS (Iterative Deconvolution and Stacking, Zhang et al., 2014) combines an iterative seismic network inversion with back projection techniques to retrieve subfault source time functions. The pseudo dynamic rupture model (Dahm et al., in review) links the rupture front propagation estimate based on the Eikonal equation with the dislocation derived from a boundary element method to model dislocation snapshots. We used the latter in both a fast rupture estimate and a fully probabilistic source inversion.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We use some Mw &amp;gt; 6.3 earthquakes that occurred in the coastal range of the Aegean Sea as an example for comparison: the Mw 6.3 Lesbos earthquake (12 June 2017), the Mw 6.6 Bodrum earthquake (20 July 2017), and the recent Mw 7.0 earthquake which occurred at Samos on 30 October 2020. The latter earthquake and the resulting tsunami caused fatalities and severe damage at the shorelines of Samos and around the city of Izmir, Turkey.&amp;lt;br&amp;gt;The fast estimates are based on only little data and/or prior information obtained from the regional seismicity catalogue and available active fault information. The large number of seismic (broadband, strong motion) and geodetic (high-rate GNSS) stations in local and regional distance from the earthquake with good azimuthal coverage jointly inverted with InSAR data allows for robust inversion results. These, and other solutions, are used as a reference for the comparison of our fast source estimates.&amp;lt;br&amp;gt;Preliminary results of the slip distribution and the source time function are in good agreement with modelling results from other authors.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We present our insights into the kinematics of the chosen earthquakes investigated by means of joint inversions. Finally, the accuracy of our first fast source estimates, which could be of potential use in tsunami early warning, will be discussed.&amp;lt;/p&amp;gt;
Applying social judgment theory to better understand what peer reviewers pay attention to when evaluating fellowship grant proposals

While research has revealed startling inconsistencies and lack of agreement in reviewers’ evaluations of grant proposals (Pier et al., 2018), the reasons why such inconsistencies arise remain unclear. In this talk, I will present results from the first phase of the “Towards Outstanding Research Reviews” (TORR) project” (TORR) project, which aims to understand how individual peer reviewers use and combine information in written grant proposals to make their recommendations for funding. Informed by the Social Judgment Theory (SJT) framework (Hammond, 1977), we conceived reviewers’ recommendations as resulting from the application of a professional “judgement policy” and we first sought to infer what research quality factors expert reviewers pay attention to while reviewing a proposal for funding in a semi-naturalistic setting. To do so, we recorded think aloud protocols from 7 reviewers who were re-examining a grant application for the Wellcome Trust’s Humanities and Social Sciences Fellowship funding scheme, which they had recently reviewed. The think aloud protocols were adapted from the Critical Decision Making method of interviewing (Klein, 1989) and the Cognitive Event Analysis framework (Steffensen et al., 2016). Following a content analysis of the transcripts, we identified a total of 56 possible quality criteria mentioned by reviewers, which were organised around 5 areas of quality. Next, we conducted a survey where a further 28 expert reviewers were asked to rank the quality criteria in order of importance. Contrary to earlier findings suggesting lack of consensus between peer-reviewers, we uncovered ten quality factors which were consistently rated as most important for assessing the quality of a fellowship application. Upon presenting the factors identified, I will discuss implications advancing the understanding of peer-review judgements, the peer-reviewers’ dilemmas we uncovered through this process, as well as the challenges and opportunities involved in meta-research studies.
Video Lightening with Dedicated CNN Architecture

Darkness brings us uncertainty, worry and low confidence. This is a problem not only applicable to us walking in a dark evening but also for drivers driving a car on the road with very dim or even without lighting condition. To address this problem, we propose a new CNN structure named as Video Lightening Network (VLN) that regards the low-light enhancement as a residual learning task, which is useful as reference to indirectly lightening the environment, or for vision-based application systems, such as driving assistant systems. The VLN consists of several Lightening Back-Projection (LBP) and Temporal Aggregation (TA) blocks. Each LBP block enhances the low-light frame by domain transfer learning that iteratively maps the frame between the low- and normal-light domains. A TA block handles the motion among neighboring frames by investigating the spatial and temporal relationships. Several TAs work in a multi-scale way, which compensates the motions at different levels. The proposed architecture has a consistent enhancement for different levels of illuminations, which significantly increases the visual quality even in the extremely dark environment. Extensive experimental results show that the proposed approach outperforms other methods under both objective and subjective metrics.
Factor of Active Participation Engineering and Technical Student-Athletes in Co-Curricular Activities (Sport)

Active participation among engineering and technical student- athletes in co-curricular activities is viewed from different side which is some of them were represent national, universities, faculties in sporting event. Therefore, the purpose of this research was to examine the factors that encourage active participation student participation in co-curricular activities (sports) among engineering and technical students of University Tun Hussein Onn Malaysia (UTHM) who are participate actively in co-curricular activities (sports). Identifying the factors and perception of active participation engineering and technical student- athletes in co-curricular activities (sports) are the main focus on this study. Quantitative research design by using survey were approach on this study. Survey were conducted into two hundred and thirteen student of engineering and technical student-athletes UTHM who are participate actively in co-curricular activities (sports). Findings shows student- athletes agreed that interpersonal, intrapersonal and structural factors are the reason why they involve in co-curricular (sports) activities. These findings could help the university to create active participation environment among engineering and technical student- athletes in co[1]curricular activities (sports) and at the same time help on producing student-athletes who are able represent university and also national at international levels.
PERKIRAAN LUAS KERUSAKAN PERMUKAAN JALAN DENGAN MENGGUNAKAN METODE THRESHOLD

The soils in Banjarmasin and surrounding areas are frequently found to have low bearing capacity, because the areas are normally submerged in water and the soils are very soft. As a result, road damages take a place faster in these soft subgrade conditions. Information about the dimension of road surface damage is one of the main parameters in the road pavement maintenance and management systems. The dimension of surface area of a damage road is one of the determination factors to assess the level of road conditions and also to estimate the rehabilitation needs. The conventional method for surveying road damage is usually done on foot where the surveyor records the dimensions of damage encountered at a particular locations. Today, with the development of digital camera technology and also the advancement of computer science, the method of survey has shifted from manual to automatic with the utilization of cameras. Therefore, it can be done faster and more accurate. This study applies the thresholding method in computing the number of pixels in an image to estimate the area of road damage.
An improved Weber-face-based method for face recognition under uncontrolled illumination conditions

The paper presents a new face recognition system robust to illumination variations and moderate occlusion. Two main contributions are discussed. First, we introduce an approach based on contrast equalisation (CE) to improve the traditional Weber-face (WF) technique and make it more robust. Second, we use the local binary patterns (LBP) and local phase quantisation (LPQ) descriptors to make the Weber-face method more resilient to variations in illumination by exploiting both spatial and frequency domains information. By combining the two descriptors, enhanced facial features are obtained showing more discriminating power for variable lighting conditions as well as occlusion. The concept of complementing the WF model with spatial-frequency descriptors is novel and shown to result in a robust system resilient to changing lighting conditions, variations in pose, and occlusion. The method was compared to a number of existing techniques over three public databases. The proposed algorithm outperformed existing techniques under challenging environments.
Force-Directed Embedding of Scale-Free Networks in the Hyperbolic Plane

Force-directed drawing algorithms are the most commonly used approach to visualize networks. While they are usually very robust, the performance of Euclidean spring embedders decreases if the graph exhibits the high level of heterogeneity that typically occurs in scale-free real-world networks. As heterogeneity naturally emerges from hyperbolic geometry (in fact, scale-free networks are often perceived to have an underlying hyperbolic geometry), it is natural to embed them into the hyperbolic plane instead. Previous techniques that produce hyperbolic embeddings usually make assumptions about the given network, which (if not met) impairs the quality of the embedding. It is still an open problem to adapt force-directed embedding algorithms to make use of the heterogeneity of the hyperbolic plane, while also preserving their robustness.We identify fundamental differences between the behavior of spring embedders in Euclidean and hyperbolic space, and adapt the technique to take advantage of the heterogeneity of the hyperbolic plane.
Keyphrase Graph in Text Representation for Document Similarity Measurement

To represent the text document more expressively, a kind of graph-based semantic model is proposed, in which more semantic information among keyphrases as well as the structural information of the text are incorporated. The method produces structured representations of texts by utilizing common, popular knowledge bases (e.g. DBpedia, Wikipedia) to acquire fine-grained information about concepts, entities, and their semantic relations, thus resulting in a knowledge-rich interpretation. We demonstrate the benefits of these representations in the task of document similarity measurement. Relevance evaluation between two documents is done by calculating the semantic similarity between two keyphrase graphs that represent them. Experimental results show that our approach outperforms standard baselines based on traditional document representations, and able to come close in performance to the specialized methods particularly tuned to this task on the specific dataset.
An Ensemble Clusterer Framework based on Valid and Diverse Basic Small Clusters

Clustering ensemble is a new problem where it is aimed to extract a clustering out of a pool of base clusterings. The pool of base clusterings is sometimes referred to as ensemble. An ensemble is to be considered to be a suitable one, if its members are diverse and any of them has a minimum quality. The method that maps an ensemble into an output partition (called also as consensus partition) is named consensus function. The consensus function should find a consensus partition that all of the ensemble members agree on it as much as possible. In this paper, a novel clustering ensemble framework that guarantees generation of a pool of the base clusterings with the both conditions (diversity among ensemble members and high-quality members) is introduced. According to its limitations, a novel consensus function is also introduced. We experimentally show that the proposed clustering ensemble framework is scalable, efficient and general. Using different base clustering algorithms, we show that our improved base clustering algorithm is better. Also, among different consensus functions, we show the effectiveness of our consensus function. Finally, comparing with the state of the art, we find that the clustering ensemble framework is comparable or even better in terms of scalability and efficacy.
Formation of systematic knowledge in science students in higher education

The study deals with the main issues of developing systematic knowledge in students within higher education and provides possible solutions. A scheme of systematic approach stages is proposed. Particular attention is given to examples from pedagogical practice.
Exploring Robust Misclassifications of Neural Networks to Enhance Adversarial Attacks

Progress in making neural networks more robust against adversarial attacks is mostly marginal, despite the great efforts of the research community. Moreover, the robustness evaluation is often imprecise, making it difficult to identify promising approaches. We analyze the classification decisions of 19 different state-of-the-art neural networks trained to be robust against adversarial attacks. Our findings suggest that current untargeted adversarial attacks induce misclassification towards only a limited amount of different classes. Additionally, we observe that both over- and under-confidence in model predictions result in an inaccurate assessment of model robustness. Based on these observations, we propose a novel loss function for adversarial attacks that consistently improves attack success rate compared to prior loss functions for 19 out of 19 analyzed models.