Reviewerly/exHarmony huggingface dataset:
A digital learning future

Across the world, digital education is becoming more and more important. It is continuing to grow in popularity among learners and teachers alike and, against the backdrop of COVID-19, has proven vital to enable training and educational development to continue. The European Commission has introduced a new Digital Education Action Plan 2021-2027 that is designed to overcome challenges associated with delivering digital education. It builds on the work of the previous 2018-2020 Action Plan and incorporates learnings from the COVID-19 pandemic. The idea is for world class, inclusive and accessible education to be available digitally to students across Europe. This will involve fostering stronger cooperation between key stakeholders in Member States. Indeed, a strategic dialogue will be launched among EU Member States in order to develop a Council Recommendation on factors that will help facilitate digital education by 2022. A priority area is how digital education can be optimised in education, creating a digital education system that is high-performing and universally useful. A second priority area concerns the development of digital skills and competencies in learners and teachers alike, as well as ensuring online learning resources are relevant, interactive and user-friendly.
Vision-Based Fault Action Detection for Industrial Robots

With the large-scale application of industrial robots, the safety problems caused by the sudden failures of industrial robots occur from time to time.Traditional fault diagnosis methods based on data analysis have several problems, such as the sensor data are disturbed by the external environment, the communication protocols for different types of robots are complex and not unified, the monitoring systems embedded in the execution systems affect each other, and so on.In this paper, we propose a vision-based fault detection method for industrial robots.According to this method, we perform real-time analysis of operation video from industrial robots.An image segmentation module extracts the body part of an industrial robot, and an image hashing module then generates the posture code describing the movement of the industrial robot.Thus, we can obtain early warnings based on abnormal movements of the robot, which can be detected by the sequence pattern analysis technology.The proposed method does not rely on communication protocols of industrial robots and can monitor industrial robots in a real-time non-contact manner.Besides, the method is easy for deployment at a low cost.We conduct experiments on a collected dataset containing videos of industrial robots.Experiment results show that the proposed method can accurately identify the abnormal actions of industrial robots at an average detection accuracy of 99.15%, which meets the needs of industrial applications in practice.
Integración de datos meteorológicos hetereogéneos basada en tecnologías semánticas para la mejora de la eficiencia energética en edificios

La utilizacion de tecnologias pertenecientes a la Web Semantica, actualmente esta presentando una elevada progresion, debido a que son de gran utilidad tanto para maquinas, como para humanos. El uso de la Inteligencia Artificial, junto con la Web Semantica, han empezado a ser utilizadas por un mayor numero de empresas e instituciones, permitiendo el intercambio de documentacion entre ellos, a traves de la publicacion de datos en formato de datos abiertos. Para poder exponer, compartir y conectar conjuntos de datos, existe la tecnologia de los “datos enlazados”, que proponen normas para llevar a cabo esos procesos. Debido a la necesidad existente, en esta tesis se presenta una creacion de datos enlazados para determinadas fuentes de datos meteorologicos. Para ello, se ha desarrollado una ontologia capaz de contener toda la terminologia necesaria existente en las fuentes de datos utilizadas, que a su vez se encuentra publicada con su respectiva documentacion. Los recursos obtenidos tras realizar el proceso de transformacion, se encuentran en formato RDF, validados por medio de herramientas desarrolladas para realizar tal proposito. Se ha implementado todo el proceso de transformacion de datos a datos enlazados en un servicio web rest, con acceso en tiempo real a las fuentes de datos que se quieren transformar. Posteriormente, los resultados del proceso realizado son publicados y probados mediante queries de SPARQL. Cabe destacar que los resultados obtenidos son adecuados para la tarea propuesta.---ABSTRACT---The use of technologies belonging to the Semantic Web is currently showing a high progression, because they are very useful for both machines and humans. The use of Artificial Intelligence, together with the Semantic Web, has begun to be used by a greater number of companies and institutions, allowing the exchange of documentation between them, through the publication of data in open data format. In order to be able to expose, share and connect data sets, there is the technology of ’linked data’, which proposes standards to carry out these processes. Due to the existing need, this thesis presents a creation of linked data for certain sources of meteorological data. For this purpose, an ontology has been developed capable of containing all the necessary terminology existing in the data sources used, which in turn is published with its respective documentation. The resources obtained after carrying out the transformation process are in RDF format, validated by means of tools developed for this purpose. The entire process of transforming data into linked data has been implemented in a web rest service, with real time access to the data sources to be transformed. Afterwards, the results of the process are published and tested by means of SPARQL queries. It should be noted that the results obtained are suitable for the proposed task.
Hybrid Optimization Technique for Enhancing the Stability of Inverted Pendulum System

The inverted pendulum system (IPS) is considered the milestone of many robotic-based industries. In this paper, a new variant of variable structure adaptive fuzzy (VSAF) is used with new reduced linear quadratic regulator (RLQR) and feedforward gain for enhancing the stability of IPS. The optimal determining of VSAF parameters as well as Q and R matrices of RLQR are obtained by using a modified grey wolf optimizer with adaptive constants property via particle swarm optimization technique (GWO/PSO-AC). A comparison between the hybrid GWO/PSO-AC and classical GWO/PSO based on multi-objective function is provided to justify the superiority of the proposed technique. The IPS equipped with the hybrid GWO/PSO-AC-based controllers has minimum settling time, rise time, undershoot, and overshoot results for the two system outputs (cart position and pendulum angle). The system is subjected to robustness tests to ensure that the system can cope with small as well as significant disturbances.
Token-Level Contrast for Video and Language Alignment

Building video and language understanding models requires grounding linguistic concepts and video contents into a shared space. Most of previous works learn a holistic alignment between them while neglecting the token-level grounding. Masked token prediction can be used to learn token-level multi-modal representation, but it does not necessarily force lexical grounding on perception and also introduce a domain-shift between pretraining and fine-tuning. This paper introduces a simple token-level contrastive loss (ToCo) informed by syntactic classes (e.g., nouns and verbs) to force the model to prioritize grounding concrete semantic bearing words. ToCo does not mask inputs but poses both local (contextual token) and global (lexical type) pressures for multi-modal alignment in a contrastive manner. Our approach enables a simple vanilla BERT-based multimodal transformer to compete with or outperform existing heavily engineered multi-loss or large models on three benchmarks (YouCook2, MSR-VTT and CrossTask). Further, it is plug-n-play such that gains are made in both pretraining and downstream tasks solely, regardless of the underlying visual or textual feature representations.
La superación profesional del docente en la elaboración de las Monografías en el Instituto Superior de Angola

In contemporaneity there are constant changes that have been the result of the progressive advance of science and technology, and have responded to the processes of economic, political and ideological restructuring that occur globally.The educational reform in Angola, which raises the need to pay special attention to the permanent training of teachers, having found that this is the main agent of change to achieve a high quality in the educational processes that are aspired to.This research aims to reflect on the professional improvement of teachers to favor the quality of Monographs in the Higher Institute of Angola.
Library System: Library Access Instructions: GCTS Resource Access

This guide provides instruction on what credentials to use when prompted by the EZproxy login page.
Smart Recommendation System Based on Understanding User Behaviour for Afan Oromo Language with Deep Learning

Recommender system is an encouraging technology for enterprises to present personalized suggestions to their customers.But this technology suffers from sparsity problem.In addition, greatest researches are grounded on explicit rating.But most users do not spend time for rating of products.Therefore, this research proposes an effective recommendation based on user behavior Consumer behavior is one of the most important issues that have been discussed in recent decades.Organizations always want to understand how consumer makes decisions so that they can use it to design their products and services.Having a correct understanding of the consumers and the consumption process has many advantages.These advantages include helping managers make decisions, providing a cognitive basis through consumer analysis, helping legislators and regulators legislate on the purchase and sale of goods and services, and ultimately helping consumers make better decisions.Here is a solution for recommending goods based on the users' past behavior over deep learning.The architecture expressed for deep learning is trained by users' past behavioral data.Amazon data was studied and the results indicated that the proposed method has a much higher accuracy than similar methods.Primary contribution is implementation of a user behavior-based recommendation method that discovers interest of users based on implicit rating of product attributes.In addition, this approach uses sequential pattern of purchasing to improve the quality of recommendation.
Flexible Data Hiding and Extraction in EtC Images

This paper proposes a data hiding method for compressible encrypted images called the encryption-then-compression (EtC) images. We allocate bit planes of an original image to an encryption field and a data hiding field, where the upper 7 bits are encrypted by a compressible encryption (CE) method and LSBs are replaced with payload bits. By conducting the encryption and data hiding processes in the independent fields, the proposed method can embed and extract the payload flexibly without any complex condition and preprocessing. Experimental results and discussion show the effectiveness of the proposed method in terms of lossless compression efficiency by using JPEG-LS and JPEG 2000, marked-image quality, and robustness against attacks.
Methods for Semantic Interoperability in AutomationML-based Engineering

Industrial engineering is an interdisciplinary activity that involves human experts from various technical backgrounds working with different engineering tools. In the era of digitization, the engineering process generates a vast amount of data. To store and exchange such data, dedicated international standards are developed, including the XML-based data format AutomationML (AML). While AML provides a harmonized syntax among engineering tools, the semantics of engineering data remains highly heterogeneous. More specifically, the AML models of the same domain or entity can vary dramatically among different tools that give rise to the so-called semantic interoperability problem. In practice, manual implementation is often required for the correct data interpretation, which is usually limited in reusability.Efforts have been made for tackling the semantic interoperability problem. One mainstream research direction has been focused on the semantic lifting of engineering data using Semantic Web technologies. However, current results in this field lack the study of building complex domain knowledge that requires a profound understanding of the domain and sufficient skills in ontology building. This thesis contributes to this research field in two aspects. First, machine learning algorithms are developed for deriving complex ontological concepts from engineering data. The induced concepts encode the relations between primitive ones and bridge the semantic gap between engineering tools. Second, to involve domain experts more tightly into the process of ontology building, this thesis proposes the AML concept model (ACM) for representing ontological concepts in a native AML syntax, i.e., providing an AML-frontend for the formal ontological semantics. ACM supports the bidirectional information flow between the user and the learner, based on which the interactive machine learning framework AMLLEARNER is developed.Another rapidly growing research field devotes to develop methods and systems for facilitating data access and exchange based on database theories and techniques. In particular, the so-called Query By Example (QBE) allows the user to construct queries using data examples. This thesis adopts the idea of QBE in AML-based engineering by introducing the AML Query Template (AQT). The design of AQT has been focused on a native AML syntax, which allows constructing queries with conventional AML tools. This thesis studies the theoretical foundation of AQT and presents algorithms for the automated generation of query programs. Comprehensive requirement analysis shows that the proposed approach can solve the problem of semantic interoperability in AutomationML-based engineering to a great extent.
Implementing FP-Growth Algorithm using Map Reduce for Mining Association Rules

Abstract : In mining frequent itemsets, one of most important algorithms is FP-growth. FP-growth proposes an algorithm to compress information needed for mining frequent itemsets in FP-tree and recursively constructs FP-trees to find all frequent itemsets. Map Reduce is a distributed processing framework where the application is divided into many fragments of work, each of which may be executed on any node on a cluster. The main objective of this paper is Parallel FP-growth algorithm to achieve the quality of FP-growth. Our proposed method implemented the Parallel FP-Growth based on Map Reduce framework using Hadoop approach. New method has high achieving performance compared with the basic FP-Growth. The Parallel FP-growth algorithm can work with the large datasets to discovery frequent patterns in a transaction database. Based on our method, the execution time under different minimum supports is decreased. Keywords : itemsets, FP-tree, hadoop, map reduce, support Cite this Article K. Purushotam Naidu, Ch. V.V.D. Prasad. Implementing FP-Growth Algorithm using MapReduce for Mining Association Rules. Journal of Advanced Database Management & Systems. 2019; 6(2): 18–29p.
MASCHINELLES LERNEN FÜR PER-TITLE ENCODING

Video-Inhalte unterscheiden sich in ihrer Komplexitat – herkommliche statische Encoding-Verfahren ignorieren jedoch die individuellen Videocharakteristiken und wenden fur alle, teils sehr unterschiedliche Videodateien die gleichen Einstellungen an. Dies fuhrt zu unnotig hohem Speicherbedarf und gesteigerten Ubertragungskosten fur Streaming-Anbieter. Der Per-Title Encoding Ansatz adressiert dieses Problem und hat das Potenzial, die Speicher- sowie Ubertragungskosten von Video-Streams erheblich zu senken. Bisherige Losungen erfordern in der Regel eine grose Anzahl von Test-Enkodierungen, die entsprechende Rechenzeiten benotigen und daher zu erheblichen Mehrkosten fuhren. Dieser Artikel beschreibt eine Losung, die den konventionellen Ansatz fur Per-Title Encoding um Verfahren des maschinellen Lernens erweitert und somit in der Lage ist, vollstandig auf rechenaufwandige Test-Enkodierungen zu verzichten. //Video content differs in its complexity – and yet, conventional static encoding methods ignore the individual video characteristics and apply the same settings to all video files equally. This leads to unnecessarily high storage requirements and increased transmission costs for streaming providers. The Per-Title Encoding approach addresses this problem and has the potential to significantly reduce the storage and transmission costs of video streams. Previous solutions usually require a large number of test encodings, which require corresponding computing time and therefore lead to significant additional costs. This article describes a solution that extends the conventional approach to per-title encoding by machine learning methods and is thus able to completely dispense with computationally expensive test encodings.
LibGuides: ENGL 3050: Writing Pedagogy: Citation & RefWorks

The purpose of this course guide is to provide research and library resources for ENGL 3050.
Retraction: A Methodology for the Synthesis of E-Commerce (Journal of Physics: Conference Series 1230 012004)

This article has been retracted by IOP Publishing on 18 February 2021 in light of clear evidence that it was computer generated. IOP Publishing is investigating why this was not identified during the submission and peer review process by the conference. As a member of the Committee for Publication Ethics (COPE) this has been investigated in accordance with COPE guidelines and it was agreed the article should be retracted. Retraction published: 18 February 2021
The Program for Cooperative Cataloging in the Time of COVID-19: Part 2

Click to increase image sizeClick to decrease image size Notes1 PHE Investigating a Novel Variant of COVID-19 (2020, December 14). Retrieved from https://www.gov.uk/government/news/phe-investigating-a-novel-variant-of-covid-19. San Diego Man Tests Positive for UK Variant of COVID-19 (2020, December 30). Retrieved from https://www.countynewscenter.com/san-diego-man-tests-positive-for-uk-variant-of-covid-19/. Chart retrieved from https://www.sandiegocounty.gov/content/dam/sdc/hhsa/programs/phs/Epidemiology/COVID-19%20Cases%20by%20Date%20of%20Illness%20Onset.pdf (updated daily).2 Ed Jones, Jennifer Baxmeyer, Judith Cannan, Naun Chew, Paul Frank, and Adam Schiff, “The Program for Cooperative Cataloging in the Time of COVID-19,” Cataloging & Classification Quarterly 58, no. 8 (2020), https://doi.org/10.1080/01639374.2020.1861152.3 “Meeting & Parting,” Library Journal 146 (January 2021): 36, https://www.libraryjournal.com/?detailStory=Meeting-and-Parting-ALA-Midwinter-2021-Preview.4 Steven Riel (2020, March 24), Re: Provider-neutral successive-entry cataloging [electronic mailing list message] Retrieved from https://listserv.loc.gov/cgi-bin/wa?A2=ind2003&L=CONSRLST&X=455ADCAE12AC7C89985 Judith Cannan (2020, April 7), Re: Provider-neutral successive-entry cataloging [electronic mailing list message] Retrieved from https://listserv.loc.gov/cgi-bin/wa?A2=ind2004&L=CONSRLST&X=455ADCAE12AC7C89986 PCC Joint Operations Committee Meeting Agenda, Thursday, May 7, 2020, Library of Congress. https://www.loc.gov/aba/pcc/documents/OpCo-2020/Agenda-OpCo-2020.pdf7 PCC Policy Committee Virtual Meeting, October 28-30, 2020, Online. [Agenda] Retrieved from https://www.loc.gov/aba/pcc/documents/PoCo-2020/PoCo-2020-Agenda.docx8 Summer E. Galloway, Paul Prabasaj, Duncan R. MacCannell, et al., “Emergence of SARS-CoV-2 B.1.1.7 Lineage — United States, December 29, 2020–January 12, 2021,” Morbidity and Mortality Weekly Report. Early release: 70 (15 January 2021): 2, https://dx.doi.org/10.15585/mmwr.mm7003e2.
A Critical Analysis of Distribution Shift

We introduce three new robustness benchmarks consisting of naturally occurring distribution changes in image style, geographic location, camera operation, and more. Using our benchmarks, we take stock of previously proposed hypotheses for out-of-distribution robustness and put them to the test. We find that using larger models and synthetic data augmentation can improve robustness on real-world distribution shifts, contrary to claims in prior work. Motivated by this, we introduce a new data augmentation method which advances the state-of-the-art and outperforms models pretrained with 1000x more labeled data. We find that some methods consistently help with distribution shifts in texture and local image statistics, but these methods do not help with some other distribution shifts like geographic changes. Hence no evaluated method consistently improves robustness. We conclude that future research should study multiple distribution shifts simultaneously.
Reinforcement Learning for Control with Probabilistic Stability Guarantee

Reinforcement learning is promising to control dynamical systems for which the traditional control methods are hardly applicable. However, in control theory, the stability of a closed-loop system can be hardly guaranteed using the policy/controller learned solely from samples. In this paper, we will combine Lyapunov's method in control theory and stochastic analysis to analyze the mean square stability of MDP in a model-free manner. Furthermore, the finite sample bounds on the probability of stability are derived as a function of the number M and length T of the sampled trajectories. And we show that there is a lower bound on T and the probability is much more demanding for M than T. Based on the theoretical results, a REINFORCE like algorithm is proposed to learn the controller and the Lyapunov function simultaneously.
Studi Eksperimental Performa Mesin Suzuki Satria F 150 Menggunakan Moifikasi Diameter Dalam Magic Ring

This study aims to determine the performance level of an engine including torque, power, specific fuel consumption and temperature changes in the combustion chamber when a machine is working. The fuel motor that is still verydominant to date is the piston combustion motor as the initial driving force, which currently has not been replaced by the piston combustion engine, this is becausethe piston combustion motor is still very efficient to be used as the starting motor and the piston combustion motor still produces exhaust gas. which can be used again to improve performance. One of the innovations is the Magic Ring, the benefit of the Magic Ring itself is to make fuel consumption more efficient, the way the Magic Ring works is to block some of the heat that comes out of the Exhaust Manifold and put it back into the combustion chamber to help the combustion process in the combustion chamber. The study conducted in this study was to try the Magic Ring but with different inner diameters to determine the performance and efficiency of a machine, the results of the study proved that the modification of the inner diameter of the Magic Ring had an effect on engine 2 performance at engine speed of 6000 to 8000 RPM and lowers its specific fuel consumption and is more efficient than standard motorbikes or using the MagicRing.