Reviewerly/exHarmony huggingface dataset:
El rol del docente universitario en las propuestas educativas virtualizadas

In the last decades, the use of virtual teaching and learning environments has caused an extensive number of transformations in the teaching profession that influence practices. This work aims to make a contextualized analysis of the influence that the integration of technologies has on the profile, functions and roles that lecturers must play in the virtualized proposals of CURZA-UNCo. To that end, we made a bibliographic survey focused on contributions of the last twenty years of different authors on the topic. This survey enabled the design of a semi-structured interview, used as a data collection technique from an ethnographic perspective. We interviewed ten CURZA teachers and the results obtained in these interviews were analysed in the light of the initial theoretical framework. The findings indicate that, on the one hand, some educators do not yet understand that the 21st century demands new forms of communication, new work styles, new ways of accessing and producing knowledge. On the other hand, they also suggest that educational institutions must take management decisions based on a deep comprehension of technology crossing. They should invite teachers to rethink their roles and, consequently, they should stimulate the necessary professional development policies to make of ICT a real opportunity for change and educational improvement.
LibGuides: Academic Areas of Interest: Engineering, Math, & Computer Science

The Harper College Library supports the College’s Academic Areas of Interest with a wide range of resources, study spaces and services--including research consultations and 24/7 online chat.
Knowledge Graph Representation of Syntactic and Semantic Information

Representation of linguistic knowledge is one of the keys to helping machines understand natural languages. This paper follows the idea from linguistic data to linguistic knowledge and to knowledge representation. At the syntactic level, the syntactic structure and its variants in the corpus are summarized, and the syntactic functions undertaken by the arguments are analyzed. At the semantic level, the semantic roles and semantic types of arguments are analyzed. The purpose is to reveal the interaction between syntax and semantics. Finally, this paper explores a fusion representation method of linguistic data and linguistic knowledge, and carries out a case study.
Vision System for Item Recognition: Case Study Computer Peripherals

Vision System is part of Artificial Intelligence (AI) using the camera as a primary input. Over the past fifty years, the development of AI had achieved a great achievement such as the development of a Vision System. Implementation of the Vision System is not limited to industry but also as a solution to recognize an unknown object in various area. However, the availability of the current Vision System for personal usage is still limited in the market (such as Google Lens and CamFind) which is known to have a harsh requirement to whom able to use it or had limited functionality that result of unable to provide more information on the unrecognized item. Therefore, this research is proposed to create an alternate Vision System which enhancing user experience on the computer peripherals. A forum-like social environment is also develop as extra channel for the user to share information. The user will able to use this item recognition function to identify the computer peripherals that they are interested to find out and post their doubts to the forum in getting help from others.
A Supervised Clustering Algorithm Base on Alternative distance for Using multimedia software to Identify Alzheimer’s disease

Fuzzy algorithms of GG and GK based on Mahalanobis distance can improve those limitations of roller bearing architectural classifications, but Gath and Geva clustering algorithm could only use the data with multi-dependent variable normal distribution. GK-algorithm is limited as it must know the distribution of data. The data set was set by Min-Hwei College of Health Care Management in Taiwan and is a complete test on medical staff and skill of medical care. Hence we can collect the student data in the classes where they have good abilities. This discussion is about supervised classification method constructing the fuzzy clustering by means of putting forward method. Our discussion about an updated essential value not diverging calculation method to promote the prevention of Gath-Geva or Gustafson-Kessel methods, removed the limit of the determinants of common variance of matrices in the Gustafson-Kessel methods, and alternative for the common variance matrix with the statistics of related matrix which exists in the main function. The experimental results of real data sets of using multimedia software to identify Alzheimer's disease and then to prevent aging prove that the supervised classification method constructing the fuzzy clustering by means of method obtain much better result, when data dispersion between different clusters is overlapping or the shape of clusters is not roller bearing.
Impacts of Artefacts and Adversarial Attacks in Deep Learning based Action Recognition

Current state-of-the-art deep learning-based models for human action recognition achieve impressive accuracy on benchmark datasets. However, the fact that those models are trained and tested on “clean” and high-quality input data raises a concern about their reliability under transmission artefacts and adversarial perturbations. In this work, we conduct for the first time an evaluation of the impacts of artefacts and adversarial attacks in deep learning-based human action recognition. Findings from this evaluation provide insights into the behaviors of action recognition under hostile conditions of best-effort networks.
Weighted Personalized Factorizations for Network Classification with Approximated Relation Weights

Classifying Multi-Label nodes in networks is a well-known and widely used task in different domains. Current classification models rely on mining the network structure either by random walks or through approximating the laplacian of the network graph which gives insight about the nodes’ neighborhood. In implicit feedback relations, these models assume all relation edges to be equally strong and important. However, in real life, this is not necessarily the case as some edges might have different semantic weights such as friendship relation. To tackle this limitation we propose in this paper a weighted two-stage multi-relational matrix factorization model with Bayesian personalized ranking loss for network classification that utilizes different weighting functions for approximating the implicit feedback relation weights. Experiments on four real-world datasets show that the proposed model significantly outperforms the state-of-art models. Results also show that selecting the right weighting functions for approximating relation weights significantly improves classification accuracy.
Development of a Sensor-embedded Device for Infant Health Monitoring

Infant health monitoring leads to lighten the burden of child care. According to Hyogo medical association, the observation of multiple behaviors is needed for estimating infants health conditions. This study develops a sensor-embedded plushie device for frequent observation of four kinds of behaviors in temper tantrum. To make the device more familiar for infants, the device is based on an idea of“security blanket”which is favorite item for infants. Especially, small-sized and lightweight sensors are developed: a tensile force sensor and an omnidirectional bending sensor. Taking into account sanitation, the outer cover of device is detachable and parents can wash it. An experiment conducted with adult subjects examines the basic measurement performance of the device. Experimental results demonstrate the possibility to distinguish the behaviors in normal and temper tantrum conditions.
A neural-attenuation model before Mexican extreme events

The most recent shaking experiences have demonstrated that the predictions of the seismic models are not always in agreement with the registered responses, especially in the face of extreme earthquakes. Records collected from 1960 to 2011 at a rock-like site are used to develop a neural network that permits to estimate peak ground accelerations via the magnitude, the focal depth, the site-source distance and a seismogenic zone. The neural model is applied to the 8th and 19th September 2017 events that hit Mexican territory and the obtained results show that the network is flexible enough to work appropriately to various conditions of intensity and sites-sources with remarkably predictive capacity. The neural-attenuation curves are compared with those obtained from Ground Motion Prediction Equations and their performance is assessed for events, in addition to the devastating Mexican events, from Japan, Taiwan, Chile and USA.
Application of Collision Detection Algorithm and Scoring Health Point in Fighting Games with Android-Based Augmented Reality Technology

Games are one of the media of technology that is popular among the public, both from children and adults as an entertainment medium. The game itself has a lot of genres or types. One of the types discussed in this study is developing a fighting type game by applying the Augmented Reality (AR) system in making games. Similar games that have been observed have weaknesses where the AR game flow concept is not clearly stated, there are some similarities in the marker but different Attack stats, and there is no one that has raised the character of Betta fish. For this reason, it is necessary to make a similar game by applying a marker-based tracking technique and lifting the game of betta fighting by applying AR technology. In this study, applying the Collision Detection algorithm to detect collisions between objects. In this study succeeded in making Betta Fish Fighter with the Multimedia Development Life Cycle (MDLC) Method. Based on the tests that have been carried out, the alpha test results are functionally appropriate and from the beta testing the results of the User Acceptance Test (UAT) functionalities obtained a value of 90.66% declared feasible to use with the interpretation of “Good” which means the game is suitable developed.
Analytics and visualization of spatial models as a service

The Cell-DEVS WebViewer is a lightweight, web-based software that allows users to visualize, interact with and analyze results of cellular automata based simulators such as CD++. A first version of this platform was presented at SpringSim 2018, since then, many improvements have been added. Notably, it has been refactored to follow a more object-oriented architecture which is presented in detail in this paper. Other improvements include additional analytical features as well as an integration with RISE, a server-side distributed simulator that allows users to remotely manage the complete lifecycle of a simulation. The aim of this platform is to support stakeholders and decision makers by providing them with a simple, user-friendly graphic user interface that offers a deeper insight into the system under study.
Object Security Scheme based on Access Policies using MQTT Protocol for IoT Devices

The revolutionary innovations in IoT technology, is making our life easy to operate remote things at a fingertip through globally interconnected devices. But the flip side of this technology is that these connected devices are highly to security threats. The researchers are exploring the efficient security solutions in aspect of device management, encryption, and access policy to make IoT architecture safer to communicate in real-time environment. As these solutions are tested on test-bench, not on real-time environment for production development, hence not reliable to implement at practical Industrial applications. This paper studies the limitations of security solutions at IoT device development and presents a practical security solution for IoT, where data access is granted based on access policies defined by the OEM. The performance is evaluated in real-time environment.
RConnected Middleware: Location based services for IoT environments

At present, the current architecture of Internet of Things (IoT) still relies on cloud infrastructures to provide services to people, even when there is direct communication between users and things. This presents challenges regarding scalability, spontaneous interaction and communication without infrastructure. Also the user interaction with IoT devices has limitations with respect to efficiency, costs and privacy. The provisioning of local services with a direct point-to-point communication link may help to overcome this concerns. Though, this imposes new challenges for developers regarding the communication and programming models. In this paper, we present RConnected, a middleware that implements autonomous interaction between Things and users mobile devices, allowing the provisioning of services and facilitating the development of mobile applications.
An Interactive Data Visualization Tool for Observing History of Fire Incidents

Data Visualization is designed to help digest information easier through graphical images instead of just plain text and numbers. Also, there is corresponding descriptive analysis on each data presented on the visualization tool. The web based interactive visualization tool developed is to help the IIS Regional department of the BFP-NCR Regional Branch generate statistical reports easier as compared to the current process that they are using wherein multiple MS excel-based reports will be generated from hard copy reports submitted by the local and municipal branches of each fire districts during post fire incident. As a result, generating the statistical reports will be harder since it will consume more time before generating the report because they will be referring at multiple MS excel file reports. This study aims in developing an interactive data visualization tool for the Investigation and Intelligence Section of the BFP-NCR Regional branch for history of fire incidents in Metro Manila in the year 2015-2018. The study is about a web-based interactive visualization tool in which users will filter to visualize the fire incidents based from charts, graphs, and mapping on a monthly or yearly basis, generate annual statistical reports and compare yearly or monthly fire incidents from the span of 2015-2018.
Predictive System Based Multi-layered Clustering Model and Least Absolute Shrinkage and Selection Operator (LASSO)

Prediction system is one of the tools that can support business decision making. However, existing prediction systems often give high error rate and high computational complexity. In order to make an accurate prediction system while reducing computational complexity, we proposed works that combines multi-layered clustering model using k-means++ as a technique to model data, and feature selection LASSO to predict variables. K-means++ is chosen as clustering method because of its simplicity and its solution through poor initialization, while LASSO is chosen as feature selection because of its good accuracy and its linear nature that cause model complexity to be simpler than non-linear feature selection. First, k-means++ is applied in each layer, then centroids from each layer are transferred into a central processor. Next, a set of centroids is then proceeded using feature selection LASSO to predict variable. Simulation results show that the proposed prediction system has given a good prediction accuracy with MAPE 23.45% and has reduced computational complexity by 89.77%.
Keefektifan Penggunaan E-Learning Berbasis Google Docs Terhadap Hasil Belajar Mahasiswa Pada Mata Kuliah Teknologi Informasi Di Stikes Siti Hajar

Abstract Background: This study aims to analyze and describe the effectiveness and activeness of students in the use of Google Docs-based E-Learning in learning on learning outcomes in the Information and Communication Technology courses at the Siti Hajar STIKes Method:  This research  used experimental research design with pretest-posttest group random control pattern, wherein this design a group of subjects was taken into the experimental group and the control group. with the Physiotherapy Student 2017/2018 Learning Year population. The data analysis method used in this study is descriptive analysis, preliminary data analysis, and final data analysis Conclusion: The application of Google Docs-based E-Learning has proven to be effective and increases student activity and learning outcomes. It can be seen from the increasing percentage of students' activeness from the medium to high category and the improvement in student learning outcomes from the quite good categories.
Adaptive Higher Degree Cubature Quadrature Information Filter for Multiple Sensor Fusion

This paper presents an improved estimation algorithm suitable for multiple sensor fusion in the critical situation where the sensors remain uncharacterized. The proposed new algorithm for nonlinear information filtering which is developed based on higher degree Cubature Quadrature rule can ensure successful sensor fusion by online adaptation of unknown sensor noise covariance. The adaptation algorithm designed for the filter guarantees positive definiteness of the adapted sensor noise covariance. Superiority of the proposed filter over its non-adaptive variant is demonstrated with the help of Monte Carlo simulation for a benchmark aircraft tracking problem. Relative performance analysis of the proposed filter with respect to a competing information filter also indicates its priority as a candidate estimator.
A Study of the Engineering Maintenance Services of a Tertiary Care Hospital

The engineering maintenance service is required in every hospital. It is more required as the hospital starts getting older. The study emphasis on the engineering maintenance service that is presently being carried on in the hospital and changes are recommended based on the study. The study has been done with the help of interviews and prospective study of the engineering maintenance happening in the hospital.
The Impact of Architecture on the Deep Neural Networks Training

Deep neural networks are able to solve much more complex and nonlinear problems than very popular but shallow technologies such as ELM, SVR or SLP. Despite of their power deep neural networks are difficult to apply due to problems with effective and successful training caused by `vanishing' problem. The paper shows that these problems can be reduced by using appropriate network architecture. The paper presents the influence of the neural network architecture on the training effectiveness and the training time. Selected network architectures such as BMPL, FCC and MLPL are described. Presented experimental results of research confirming the significant influence of architecture on the success of network training.
Research of PM2.5 Real-Time Prediction Model in Spark Cluster Environment

Big data technologies provide new ideas and means for statistical prediction of environmental air quality. In this paper, how to construct PM2.5 real-time prediction model for monitoring stations by using R language in Spark clusters is studied. Real-time data of monitoring stations stored in traditional relational database are converted into target dataset which can be put into cluster and processed by R language. The correlation analysis of pollutants and meteorological parameters that affecting the PM2.5 is carried out, so that to determine the input variables of multiple linear regression for constructing PM2.5 real-time prediction model. In spark cluster environment, Sparklyr and MLib packages are used by R language to construct prediction models for monitoring stations, each model is evaluated by four aspects such as residual analysis, significance detection, decision coefficient and test set prediction to justify its effectiveness. The experiment result shows that the model can be used to predict PM2.5 real-time value accurately.
So you've corrected someone's grammar on the internet...

Presentation given at Skepticon (Australian Skeptics' National Convention) 2019, Melbourne, Australia, December 8.
The Anglo-American Authority File: A PCC Story

This article examines the motivations for the collaboration between the British Library and Library of Congress to develop a joint (Anglo-American) authority file. It describes the obstacles that had to be overcome for the British Library to become a Name Authority Cooperative (NACO) “copy holder”, or node. It considers the contribution the British Library made to NACO, the benefits it has derived from participation in Program for Cooperative Cataloging (PCC), and concludes by looking ahead to the next 25 years.
Libguides: Open Access at CBS: Research Funding and Open Access

In this LibGuide you will find everything related to Open Access at Copenhagen Business School. Navigate by clicking on the blue tabs.
Cross-Lingual Entity Linking in Wikipedia Infoboxes

Infoboxes in Wikipedia are valuable resources for extracting structured information of entities, several large-scale knowledge graphs are built by processing infobox data, including DBpedia, YAGO, etc. Entity links annotated by hyper-links in infoboxes are the keys for extracting entity relations. However, many entity links in infoboxes are missing because the mentioned entities do not exist in the current language versions of Wikipedia. This paper presents an approach for automatically linking mentions in infoboxes to their corresponding entities in another language, when the target entities are not in current language. Our approach first builds a cross-lingual mention-entity vocabulary from the cross-lingual links in Wikipedia, which is then used to generate cross-lingual candidate entities for mentions. After that, our approach performs entity disambiguation by using a cross-lingual knowledge graph embedding model. Experiments show that our approach can discover cross-lingual entity links with high accuracy.
A Fast and Efficient Datapath Designs of Lightweight Cipher RoadRunneR on FPGA’s for Resource Constrained Environments

Standing on the verge of IoT boom, the number of low-end embedded devices is proliferating. This revolution ultimately raises the question of data security. Since these devices use low-end microcontrollers, conventional data security measures cannot be applied for such devices with low computing power. Hence lightweight cryptography comes into picture to take on the responsibility of data security. Light weight block cipher RoadRunneR was presented by Adnan Baysal as an efficient light weight block cipher with its security provable against linear and differential attacks. This paper presents the hardware implementation of RoadRunneR block cipher and the results with their respective architectures. RoadRunneR cipher has been implemented with 8-bit, 16-bit and 32-bit datapath size on four different FPGA platforms with same implementation conditions. Performance metrics of architectures is summarized and is compared with standard block ciphers such as AES [1], PRESENT [2], LED [3], ICEBERG [4], SEA [5], PRIDE [6] and RECTANGLE [7] to give the reader an in-depth insight on the performance of lightweight block cipher.