Reviewerly/exHarmony huggingface dataset:
Data Clustering With Actuarial Applications

Data clustering refers to the process of dividing a set of objects into homogeneous groups or clusters such that the objects in each cluster are more similar to each other than to those of other clusters. As one of the most popular tools for exploratory data analysis, data clustering has been applied in many scientific areas. In this paper, we give a review of the basics of data clustering, such as distance measures and cluster validity, and different types of clustering algorithms. We also demonstrate the applications of data clustering in insurance by using two scalable clustering algorithms, the truncated fuzzy c-means (TFCM) algorithm and the hierarchical k-means algorithm, to select representative variable annuity contracts, which are used to build predictive models. We found that the hierarchical k-means algorithm is efficient and produces high quality representative variable annuity contracts.
Weighted Line Graph Convolutional Networks

Line graphs have shown to be effective in improving feature learning in graph neural networks. Line graphs can encode topology information of their original graphs and provide a complementary representational perspective. In this work, we show that the encoded information in line graphs is biased. To overcome this issue, we propose a weighted line graph that corrects biases in line graphs by assigning normalized weights to edges. Based on our weighted line graphs, we develop a weighted line graph convolution layer that takes advantage of line graph structures for better feature learning. In particular, it performs message passing operations on both the original graph and its corresponding weighted line graph. To address efficiency issues in line graph neural networks, we propose to use an incidence matrix to accurately compute the adjacency matrix of the weighted line graph, leading to dramatic reductions in computational resource usage. Experimental results on both real and simulated datasets demonstrate the effectiveness and efficiency of our proposed methods.
Implementation of digital strategy in higher educational institutions in India

The higher education in India grew out of a colonial education system established by the British rule in the early 20th century. An appraisal shows that while there has been an impressive progress in taking education to the masses, yet many aberrations have crept into the delivery and management. Government has an ambitious strategy to bring in digital enablement in India's education delivery space. However, implementation of these strategies lacks pace and reach. The study looked at the digital initiatives in Organization for Economic Cooperation and Development, Chinese and Australian institutions. This provided some solutions for implementation of digital strategies. Study was carried out through inputs from various stakeholders. Results reveal that institutes lack vision, preparedness and commitment for technology implementation. The study can be further extended to control and monitoring mechanisms to realise the strategic vision of digital universities and a higher gross enrolment ratio with quality education.
Is Joystick-controlling better than Walking-around in Navigating a Virtual Space?

The advancement of virtual reality technology offers various locomotion options that support users’ navigation behaviors in a virtual reality environment. This study was aimed at examining the effects of two navigation methods—joystick-controlling and walking-around—on users’ perceived usability, behavioral engagement, and virtual presence. Fifty South Korean college students were recruited in the study, and they were assigned randomly to one of the two navigation conditions. Participants from each group were asked to observe a 3D object and complete the surveys. They were then asked to repeat the procedure with a 2D image. Using repeated-measures ANOVAs and MANOVA, we found that users using joystick-controlling reported higher usability and showed superior performance to the walking-around group on two tasks. Participants reported a higher behavioral engagement when observing the 2D image. Besides, they perceived a significantly higher virtual presence when observing the 2D image. Finally, we discussed the implications of the findings for the navigation method design.
Cortical Patterning of Morphometric Similarity Gradient Reveals Diverged Hierarchical Organization in Sensory-Motor Cortices

The topographic organization of the cerebral cortex has been proposed to provide hierarchical axes, as gradients, which revealed systematic variations of brain structure and function. However, the hierarchical organization of macroscopic brain morphology and how it constrains cortical function along the organizing axis is still unclear. We mapped systematic axes (gradients) of cortical morphometric similarity connectome, which combined multiple morphometric features conceptualized as a “fingerprint” of an individual’s brain. We observed that the principal gradient was anchored by motor and sensory cortices at two extreme ends, which were stable across numbers of feature combinations and reproducible across independent samples. Importantly, in contrast to canonical functional gradient, the morphometric gradient diverged in motor and sensory hierarchies, and that was consistent to laminar histological thickness gradient. Furthermore, the morphometric and functional gradients dissociated in the higher-order association cortices, like the fronto-parietal and default mode networks. Finally, the spatial pattern of morphometric gradients was closely associated with fundamental properties of cortical organization, including cyto- and myeloarchitectural profiles, as well as evolutionary expansion. These findings help understand the hierarchical organization of morphological markers of cortical structure.
Professional vision in and on action: Two sides of the same coin?

Professional vision, albeit a well-researched concept, has mostly been researched as professional vision on action, i.e. not in the act of actual teaching. Modern technologies, such as eye-tracking, allow us to explore also professional vision in action, i.e. when teachers actually teach (e.g. Cortina et al., 2015). This, however, bids the question how professional vision on action and in action differ. Our study aims to explore this using two methods of eye-tracking: we asked teachers to wear SMI 60 Hz eye-tracking glasses (ETG) during instruction. Later, sequences from a video tape of the same instruction (camera aimed at students) were replayed to the same teachers and their eye-movements were monitored (RED 250 mobile 250 Hz – remote ET). They were also asked to comment on the videos. Bearing in mind that these two viewpoints are very different, we opted for an in-depth analysis of the gaze replays from ETG and remote ET (concurrent viewing). This analysis takes into account the timeline of the video, the context, the events, and also the teacher’s comments.
Smart Factories, Dumb Policy? Managing Cybersecurity and Data Privacy Risks in the Industrial Internet of Things

There is a great deal of buzz surrounding the Internet of Things (IoT), which is the notion, simply put, that nearly everything not currently connected to the Internet from gym shorts to factories soon will be. The Industrial Internet of Things (IIoT) is one application of this trend and involves the use of IoT technologies in manufacturing. It holds the promise to revolutionize manufacturing, but there are a number of outstanding cybersecurity and data privacy issues impacting the realization of the myriad benefits promised by IIoT proponents. This white paper analyzes some of these, focusing on: (1) critical infrastructure protection and cybersecurity due diligence, (2) trends in transatlantic data privacy protections, and (3) the regulation of new technologies like artificial intelligence (AI) and blockchain. The paper concludes with a list of options for state and federal policymakers to consider in an effort to harden the IIoT along with the supply chains critical to the continued development of smart factories.
2D Animation & Creation

Students will use Adobe Illustrator (Ai) and Adobe After Effects (Ae) and will have an understanding of how to use these industry professional programs, create characters and be able to further develop their skills.
Teleport Graph Convolutional Networks

We consider the limitations in message-passing graph neural networks. In message-passing operations, each node aggregates information from its neighboring nodes. To enlarge the receptive field, graph neural networks need to stack multiple message-passing graph convolution layers, which leads to the over-fitting issue and over-smoothing issue. To address these limitations, we propose a teleport graph convolution layer (TeleGCL) that uses teleport functions to enable each node to aggregate information from a much larger neighborhood. For each node, teleport functions select relevant nodes beyond the local neighborhood, thereby resulting in a larger receptive field. To apply our structure-aware teleport function, we propose a novel method to construct structural features for nodes in the graph. Based on our TeleGCL, we build a family of teleport graph convolutional networks. The empirical results on graph and node classification tasks demonstrate the effectiveness of our proposed methods.
Possible Fluid Detection in Equivalent TI Media

Summary We consider a transversely isotropic (TI) medium obtained using Backus averaging of parallel isotropic layers. We analyze the relations between anisotropy parameters sensitive to variations of the Lamé coefficient λ among the layers. Apart from the traditional Thomsen parameters, we also consider the recently introduced φ that is more dependent on these fluctuations. Based on Gassmann&apos;s theory, we treat variations of λ as indicative of possible changes in fluid content. We suggest that the relationship between φ and the Thomsen parameter γ depends on specific fluctuations of λ. This approach may be useful for fluid detection in equivalent TI media.
Blockchain, Bitcoin and Stigmergy: An Explanation and a New Perspective for Regulation

The need for a legal framework that explains a decentralized system is compulsory to understand whether regulation could be useful and effective. Previous academic frameworks did not manage to explain the intimate development and success of a decentralized cryptocurrency system interpreting the difficult relationship among the various layers and activities. This essay tries to create a model based on swarm intelligence and stigmergy explaining the “organization without organizer”. This innovative model can be useful to obtain a new framework that accounts for the different dynamics of cryptocurrencies and how regulation could affect different classes of actors. The distinction in Essential, Native and Metamorphic actors together with the concept of the “organization without organizer” could explain some dynamics and possibility of intervention.
Är vi framme nu? - Förslaget om barnfridsbrott och dess förhållande till tidigare argument mot kriminalisering av barns bevittnande av våld

Children who witness domestic violence has been a widely debated subject in recent decades. Today it is not a crime to expose children to such violence, as the behavior does not fall within the scope of any of the general articles of the Swedish Criminal Code. The question of criminalization has been discussed in various legislative matters but has until now been rejected. Instead, several other legislative amendments have been made. However, on June 14th, 2018 the Swedish government decided to commission a committee to consider an increased criminal justice protection for children who witness domestic violence. In the final report, the committee proposed that a provision on barnfridsbrott to be included in the fourth chapter of the Criminal Code.The essay examines the arguments that were previously presented against criminalization and in favor of the alternative amendments. Taking these arguments and new aspects into account, the essay examines if a criminalization today can be considered justified. The purpose of the essay includes an account of applicable law and the principles of criminalization. The issues of the essay are mainly analyzed from a legal development perspective. This perspective has been chosen as the essay focuses on new legislation, while also examining how the issue has been dealt with before.The result shows that the alternative legislative amendments have not had the desired effects and that the arguments previously presented against a criminalization cannot be asserted in relation to the current proposal. Because of this, and the incorporation of the Convention on the Rights of the Child into Swedish law, it is found that criminalization of children witnessed of domestic violence can be considered justified today.
LibGuides: Requesting Materials from Skyline Library: Welcome to Skyline Library!

This guide is for students who need technology, books and other items from Skyline Library. It explains how to request, pick up and return materials.
Knowledge Distillation based Ensemble Learning for Neural Machine Translation

Model ensemble can effectively improve the accuracy of neural machine translation, which is accompanied by the cost of large computation and memory requirements. Additionally, model ensemble cannot combine the strengths of translation models with different decoding strategies since their translation probabilities cannot be directly aggregated. In this paper, we introduce an ensemble learning framework based on knowledge distillation to aggregate the knowledge of multiple teacher models into a single student model. Under this framework, we introduce word-level ensemble learning and sequence-level ensemble learning for neural machine translation, where sequence-level ensemble learning is capable of aggregating translation models with different decoding strategies. Experimental results on multiple translation tasks show that, by combining the two ensemble learning methods, our approach achieves substantial improvements over the competitive baseline systems and establishes a new single-model state-of-the-art BLEU score of 31.13 in the WMT14 English-German translation task.\footnote{We will release the source code and the created SEL training data for reproducibility.}
Propuesta metodológica para la implementación de herramientas de gamificación en la formación de Maestros de Primaria

Numerous studies have confirmed that gamification develops favorable scenarios for student learning by encouraging student involvement and promoting a more active role. Starting from this premise, in this article we describe an experience we developed with first-grade teacher- training students to find a learning environment that will encourage student involvement in the construction of learning in a challenging and stimulating way. Other aims of this experience are to enable students to face awkward situations responsibly, work on their social and communica- tion skills, and employ critical, creative and contextualized thinking. To evaluate the success of this experience, after its implementation we analyzed the results by comparing responses from parti- cipating students with those from a group from the same Degree and university who employed a different methodology. To gather information, we administered an internationally validated survey – the Student Engagement Questionnaire – and corroborated its validity and reliability with the participants of our study. Our main results are that this methodological experience based on gami- fication promoted a motivating teaching-learning environment, encouraged significant interaction between teachers and students, promoted self-managed learning, interpersonal and communicative skills, and developed student adaptability, while also creating a playful and dynamic atmosphere. We therefore conclude that proper planning and gamification development help to create environ- ments that enrich learning at the university level, improve the management of knowledge and trai- ning content, and develop important transversal skills for the training of future Elementary School teachers.
Automated Class Correction and Enrichment in the Semantic Web

The Semantic Web is an effort to interchange unstructured data over the Web into a structured format that is processable not only by human beings but also computers. The key backbones of Semantic Web are ontologies and annotations that provide semantics for data. Ontologies are usually created before actual data is populated. Subsequently, they can be incomplete and they often do not provide all aspects that are required for specific domains of knowledge. Additionally, Semantic Web-based ontologies usually suffer from a considerable amount of faulty facts which are known as Semantic Web data quality issues. Due to the complexity of relationships, Semantic Web data quality issues are continuously growing. This paper follows two main objectives. Firstly, it concentrates on a specific Semantic Web data quality issue that indicates incorrect assignment between instances and classes in the ontology. Secondly, the paper shows how to discover new classes which are not denied in the ontology and how to place them in the hierarchical structure of the ontology. To make ends meet, an entropy-based approach called ACE (Automated Class Corrector and Enricher) is proposed that not only evaluates the correctness and incorrectness of relationships between instances and classes but also generates new classes to enrich ontologies. The contributions of ACE have been also explained throughout the paper. Initial experiments conducted on a Semantic Web dataset demonstrate the effectiveness of the ACE.
A Study of the Lexical Complexity of Homogeneous Texts Using Stochastic Modeling and Analysis

This paper takes a system dynamic approach to study homogeneous texts where the dynamics of the lexical richness of such texts over time are of the focal concern. It is hypothesized that the progress of the lexical complexity is driven by how far away this process is from the maximum level of complexity, while is subject to the fluctuations due to the dynamic nature of the system. It is shown that the lexical dynamics of homogeneous texts can be effectively modeled by a stochastic differential equation with proper upper bounds. The linguistic validity and the statistical goodness of the model are empirically tested with the texts of CGWR. Given the ubiquity of the diffusion phenomena in various settings of language and linguistic studies (e.g. language development), the findings of the current work should provide a useful methodological reference in comparison to classic approaches such as statistical regressions.
A Convolutional Neural Network (CNN) Approach to Detect Face Using Tensorflow and Keras

Face recognition is used in a variety of aspects in the modern world. Face detection means to identify the face from a digital image. The deep neural network is considered a powerful tool as it can handle huge amounts of data .conventional neural network is one most popular tool to detect face detection. In this paper, a deep convolutional neural network (CNN) to extract features from input images. Keras is used for implementing CNN also D'lib and OpenCV for aligning faces on input images. Face recognition performance is evaluated using a custom dataset.
Fail Path Analysis on Openflow Network Using Floyd-Warshall Algorithm

Software-Defined Network (SDN) is a new concept used to solve network problems by separating the control plane and data plane in a different device. Communication between the control plane and the data plane uses the Openflow scenario. SDN has several capabilities in many network technology methods and has been implemented, among others, for routing mechanisms. In routing, there are several problems including failure path. When a fail path occurs, the system will look for another shortest path using the Floyd-Warshall algorithm. The Floyd-war shall algorithm will be implemented using the POX controller. The implementation uses a mesh topology on the mininet emulator and pox as a controller. This research method includes analysis consisting of the preparation of hardware and software requirements, and analysis of how the system works, a design consisting of mesh topology, Floyd-war shall algorithm flowchart, propagation testing, throughput, and latency testing, implementation consisting of operating system installation, Mininet, POX, Applying Mesh topology to the Mininet Emulator, and Applying the Floyd-war shall Algorithm to the POX Controller, manually calculating the Floyd-Warshall algorithm on the mesh topology and testing which consists of testing propagation, throughput, and latency. Furthermore, testing with 3 tests is propagation, throughput, and latency. In testing the throughput and latency there are 3 scenarios.
Open Source Software Development Challenges: A Systematic Literature Review on GitHub

Git is used as the distributed version control system for many open-source software projects. One Git-based service, GitHub, is the most common code hosting and repository service for open-source software projects. For researchers that study software engineering, the content that is hosted on these platforms provides much valuable data. There are some alternatives to get GitHub data such as GitHub Archive, GitHub API or GHTorrent. Among these options, GHTorrent is the most widely known and used GitHub dataset in the literature. Although there are some review studies about software engineering challenges across the GitHub platform, no review of GHTorrent dataset-specific research is available. In this study, the 172 studies that use GHTorrent as a data source were categorized within the scope of open source software development challenges and a systematic literature review was carried out. Moreover, the pros and cons of the dataset have been indicated and the focused issues of the literature on and the open challenges have been noted.
Personalized spoiler detection in tweets by using support vector machine

At present, numerous people watch prerecorded TV programs as daily leisure. Concerning soap operas or sports, the viewers may not want to be informed about the results before watching the programs; however, they may check tweets on devices, such as smartphones, some of which can accidentally include contents referring to spoilers. To avoid reading such contents, several approaches were proposed to detect spoilers in texts (both long and short ones), including tweets. In the study by Jeon et al. focused on detecting spoilers in tweets, only one person attached labels to tweets, and the labeled tweets were used to train detectors. The trained detector was tuned for one person and, therefore, could be unsuitable for others. A tweet published in the middle of a baseball game can be considered as a spoiler by some people and is not by others; therefore, a personalized detection method is preferred. However, to the best of our knowledge, none of the related studies has considered such a personalized approach. To address this problem, we propose a semi-supervised approach to detect spoilers in tweets using a Support Vector Machine (SVM) in which each user attaches labels to tweets. After that, SVM executes the same procedure for other unlabeled tweets through bootstrapping. To verify the suitability of the proposed approach to personalize detectors, we conducted an experiment in which two participants were asked to attach labels to tweets. The experimental results indicate that this approach is ef??icient for personalized detection based on the Mann-Whitney U test.