Reviewerly/exHarmony huggingface dataset:
Ontology Matching Using Neural Networks: Evaluation for OAEI Tracks

Ontology matching is a proper method to establish interoperability among heterogeneous ontologies. In this paper, we evaluate the ontology matching approach that we proposed previously and developed on a high level of accuracy. For that purpose, we performed a very detailed experimental study on six test cases of different domains from four OAEI tracks of various campaigns. The experimental results, adopting a cross-validation procedure and basing on standard evaluation measures, show a very high accuracy of matching. The proposed approach has proven its matching efficiency in front of all OAEI matching systems of the selected campaigns with major scores of all evaluation metrics adopted for all matching challenges. That permits to perfectly increase the performance of the ontology matching task.
Everett Library: QMP 100: Roadmap: Evaluating Sources

A guide to basic information resources to help Roadmap students. A brief introduction to source evaluation
The Inheritance and Innovation of Culture-the Mission of College English Teaching in the New Era in China

With the deepening of College English teaching reform, cultural teaching plays a more important role in the cultivation of langguage learners’ humanistic quality. The acquisition of English national culture can avoid the cultural conflicts and mistakes in cross-cultural interraction, while the understanding of English national culture must be based on the comparison with the local Chinese culture. The lack of second language acquisition and the obstacle of cross-cultural communication are not integrated into the teaching of Chinese traditional culture. The purpose of this paper is to explain the phenomenon of “Cultural Aphasia” in College English Teaching in China, analyze its causes, explore the ways of integrating Chinese traditional culture into English teaching, so as to change the current teaching situation, and then cultivate more English talents with Chinese cultural background, so as to better serve the construction and development of the country in the new era in China.
Effective improvement of esp students’ skills (practical aspect)

The article is devoted to practical aspects of effective development ESP students' skills for tomorrow's challenges. It is stressed that a noticeable shift towards training ESP students in universities, on how to develop global skills, has taken place. Consideration of productive approaches at ESP classes is beneficial.  In the article deliberately proved that global skills are not restricted to any particular subject on the curriculum but are transferable across all subjects, ESP in particular. The idea that much better results when ESP is provided through exposure to the language and meaningful communication in combination with systematic studies is provided. Nowadays there is a distinct tendency in which ESP teachers are aware of the importance of meaningful communication, while the rise of technology leads employers towards becoming globally interconnected, so such global skills as interpersonal communication, collaboration, critical thinking, intercultural competence and citizenship, and digital literacy are important for for future specialists. In order to operate in an international context a successful ESP course should have tools to engage students in meaningful conversations with meaningful language as well as to develop global skills. A detailed analysis of global skills and ways how to enhance these skills are given. In the context of improvement of ESP students’ skills, the idea of a learner-centered classroom is considered to be pretty effective as teacher can not only create a learner-centered classroom, but also shift teacher’s classroom position from simple lecturer to a knowledge facilitator. The article is focused on the approaches a teacher should provide to create such kind of classroom. A special attention is payed to authentic learning environment as to important element of a successful ESP course. This element is classified in details from practical point of view.
CONCEPTUAL AND MYTHOLOGICAL ANALYSIS OF LITERARY WORKS AT THE LESSONS OF UKRAINIAN LITERATURE IN SENIOR CLASSES

Сидоренко Ю
Automatic Identification of Account Sharing for Video Streaming Services

According to multiple studies, account sharing is common among subscribers of video streaming services. This leads to huge revenue loss for service providers. Although they have strong financial interests to address the problem, service providers face multiple challenges when trying to identify shared accounts. On one hand, the huge volume of unstructured and noisy data makes it hard to manually process data. On the other hand, it is legitimate for family members to share an account, from anywhere and use many devices as they want. Only these accounts which are shared outside of the household are against policies. In this paper, we propose an efficient solution to address the account sharing problem. Based on a massive volume of session data, our solution builds user profile through accumulating and representing the geolocation and device usage information. Then we estimate the account sharing risk by analyzing the usage pattern of each account. The proposed solution can identify a significant number of shared accounts and help service providers to recoup a huge amount of lost revenue.
Detection of Faux News using Machine Learning Techniques

Faux news, one among the most important new-age problems has the potential to shape the opinions and influence decisions. The false information will spread more through social medias online. Fake news is often simply explained as a bit of article which is typically written for economic, personal or political gains. Recent political events have led to a rise within the popularity and spread of faux news. This proliferation of faux news on social media and internet is deceiving people to an extent which must be stopped. Faux news cannot be easily found by humans. They have to put lot of efforts in detecting faux news. The foremost popular of such attempts include “Blacklists” of sources and authors unreliable. While these tools are useful, so as to make a more complete end to finish solution, we would like to account for harder cases where reliable sources and authors release fake news. We used tongue processing techniques for Data pre-processing and have selection. The corpus labeled real and faux data is used for classification to yield good information for decision making. Five various models of classification are used and result analysis is carried out.
Геоэкологическая оценка административных районов Брестской области и районирование её территории

Perepechaeva I.V.; Dedkova N.V. Geoecological assessment of the administrative districts of the Brest region and zoning of its territory
Формування псевдовипадкових послідовностей для приховування даних в зображеннях

In this article we consider the ways of data hiding in digital images with the use of pseudorandom sequences and the spread spectrum technique. We propose a new way of the generation of sequences, which considers statistical properties of cover-images. This makes it possible to achieve a low correlation, which provides reliable and safe data hiding in digital images. The results of experimental researches show, that the bit error rate in restored messages is significantly reduced. At the same time, the distortions of cover-images remain the same.
Prediksi Beban Listrik Jangka Pendek Menggunakan Autoregressive Integrated Moving Average (ARIMA)

Electricity is a primary need for all people throughout the world. In an era like this humans do not use electricity is said to be something that is very impossible. In daily life, electricity is used all the time in industrial activities, household, etc. Therefore, the predicted electricity load is needed to find out the biggest electricity usage per day in every hour. Therefore, the existence of this problem the authors conducted research on the prediction of electricity load using the ARIMA method with a case study of PT PJB UP Gresik
Noreen Reale Falcone Library: How to View Your Library Account Online: Home

This tutorial will show you how to view your library account online to check due dates and renew materials.
Performance, Modularity and Usability, a Comparison of JavaScript Frameworks

JavaScript frameworks have become a rapidly growing and changing industry which led to the process of choosing a suitable framework becoming increasingly difficult. This is what this project addres ...
Leveraging Trajectory Optimization to Improve Deep Reinforcement Learning, with Application to Agile Wheeled Robot Locomotion

Author(s): Bellegarda, Guillaume | Advisor(s): Byl, Katie | Abstract: There exist several approaches to robot locomotion, ranging from more traditional hand-designed trajectories and optimization-based methods, to more recent successes with deep reinforcement learning (DRL). The advantage of traditional model-based approaches lies in the ability to perform stability and robustness analyses on the generated motions and trajectories, but such methods can suffer from modeling mismatches or expensive computation times for real-time control. DRL on the other hand requires very little information to be known a priori to (eventually) find a solution, at the cost of high sample complexity (long training times) and no stability or robustness guarantees on the learned policies. This thesis seeks to bridge the gap between these two areas by injecting model-based ideas and methods into deep reinforcement learning towards efficient, robust, and explainable learned control policies.The application of this strategy is in agile wheeled robot locomotion, for example to JPL’s RoboSimian and vehicles performing drift parking. A novel wheel model is presented, which when applied in an optimization framework, naturally results in trajectories that avoid or exploit wheel slipping depending on the task. These methods demonstrate the first planned and controlled drifting maneuvers for a wheel-legged system, with additional hardware results on a model vehicle.
Vision-integrated Navigation and Integrity Monitoring for Aircraft Final Approach

This paper proposes a tightly-integrated Vision/GNSS navigation system for aircraft final approach. It consists of: i) a Kalman filter-based state estimator which handles time-delayed vision measurements by using image-trigger signals of camera device, and ii) an integrity monitoring (IM) function for sensor fault detection and Protection Level calculation. The integrity monitoring function is founded on a batch-realization of Kalman filter. The paper introduces an IM reset mechanism which re-initializes a fault detector regularly without relying on the current state estimation result, in order to remove an influence of past undetected faults on the newly reset detector. The proposed navigation system is tested on real sensor measurements, acquired onboard an unmanned aircraft in flight, with simulated GNSS faults. The test results show an improvement in fault detectability as well as in navigation performance by adding onboard vision information to classical aircraft navigation system.
The Way of Integrating Excellent Traditional Culture into Youth Education

Chinese excellent traditional culture is the most profound soft power in Chinese nation. Its ideological concept, humanistic spirit, moral norms, etc., are not only the great spiritual power of the whole Chinese nation, but also the power source for young people to possess Chinese virtues, set up firm aspirations, break the ego and achieve the ego. Integrating Chinese excellent traditional culture into youth education must focus on creating a good social and cultural atmosphere, opening compulsory courses of traditional culture in universities, improving teachers' traditional cultural literacy and perfecting social practice education.
Upaya Meningkatkan Aktivitas dan Hasil Belajar Melalui Simulation Learning dengan Model Hollywood Squares Review

Research on Efforts to Increase Activities and Learning Outcomes of the Subject of Data Presentation through Simulation Learning with the Hollywood Squares Review Model. Based on the results of the study, it was found that the application of the Simulation Learning learning method with the Hollywood Square Review learning model could increase student activity and learning outcomes in the data presentation material at MTs Miftahul Ulum This can be seen from an increase in student activity in each cycle and an increase in the average percentage of student learning outcomes from cycle I to cycle II. In the final test of the cycle I, the average percentage of student learning outcomes was 15.38% and the average percentage of tests in the second cycle was 76.92%.Keywords: Simulation Learning, Hollywood Squares Review, Student Activities, Learning outcomes
Cybersecurity Threats in Healthcare Organizations: : Exposing Vulnerabilities in the Healthcare Information Infrastructure

The healthcare industry has been brought to the limelight with the recent COVID19 pandemic. Everyday, the world waited for information from various data warehouses to report positive cases as well as effective treatment processes that help direct patient care. It was in 2009 that the HITECH Act was passed to promote and expand the adoption of health information technology, by converting paper records into computerized or electronic records to improve quality and safety of care through efficient, and timely exchange of information between the patient and the members of the healthcare team. Today, 30% of the world’s data belong to the healthcare industry. As healthcare data exponentially increase, so does the security and privacy threats to consumer’s personal and medical information. Both government and private advocacy agencies have created policies and standards to safeguard the public against cybercrimes. But what is imperative is transparency among healthcare organizations in exposing the potential vulnerabilities in their technology infrastructure so that collaborative strategies are created to address these cybersecurity threats. The Anthem security breach is provided as an example to identify weaknesses in healthcare organizations. Recommendations for healthcare organizations are provided as well as the role of consumers in the prevention of cybercrimes in healthcare is discussed.
LibGuides: Graduate Students: Getting Started @ UVic Libraries: Graduate projects

This guide highlights services, resources and tools of interest to graduate students and are available through UVic Libraries
LibGuides: Be a Research Hero: Find Books

Looking for background information, survival guides, or handbooks? Use Minerva to search for books and other items in the University Libraries' collections.
Prediksi Kecepatan Arus Laut dengan Menggunkan Metode Backpropagation (Studi Kasus: Labuhan Bajo)

One factor that is very influential on the dynamics of the waters is the speed of ocean currents. The speed of the ocean currents has an impact on activities around the coast that is for tourists to get information about the condition of the movement of the sea. One of them is in Labuhan Bajo. Labuhan Bajo is a tourist area that has a variety of natural beauty where visitors increase every year. The influence of the west monsoon wind in Labuan Bajo is very large on the condition of sea movement, especially on ocean currents. Predictions about the speed of ocean currents are very important in marine activities, especially diving because it is an effort to prevent the occurrence of things that are not desirable because of the condition of the sea that is not conducive. In this study the method used in predicting the current speed is the Backpropagation method. By testing the hidden layer nodes and the learning rate on the Backpropagation method the best MAPE results are obtained from sharing 70% of training data with 100 hidden layer nodes and the learning rate of 0.1 is 7.59%. Whereas by sharing 80% of the best MAPE training data, there are 100 hidden layer nodes and the learning rate of 0.1 is 0.57%. Then from 90% of the data sharing training data obtained the best MAPE results in the hidden layer node 100 and a learning rate of 0.4 out of 6.65%, this shows that the Backpropagation method is very well used in predicting the speed of ocean currents.
Occlusion handling in Augmented Reality context

Handling occlusion between real and virtual objects is a challenging problem in Augmented Reality (AR) applications. Incorrect and inaccurate occlusion handling may cause confusion in users’ percep ...
Entanglement TUning via Biphoton Beating

We generate biphotons with degree of polarisation entanglement that varies over a concurrence range 0.12 &lt; C &lt; 0.97, depending on the frequency of biphotons. Our interferometric scheme offers a convenient means towards the generation of arbitrary biphoton states.
Guides: Our Services: Reading Lists: Using Talis Aspire

A guide on how to create and maintain reading lists using Talis Aspire Testing new layout for info available under Talis Aspire tab
UWM Libraries Research and Course Guides: Citation Managers: EndNote Web

Includes information on how to select a citation management tool and how to transfer your library into it from RefWorks.
TIME DOMAIN THERMOREFLECTANCE (TDTR) AND PHOTODEFLECTION SIGNATURES

crystal lattice early during the exposure to tritium are highly sought to ascertain the effects of material processing and tritium interactions. Further, simple sample preparation, compared to TEM, at a significantly reduced cost is highly desirable. A pump-probe laser technique using a femtosecond laser has been demonstrated to measure the thermal diffusivity of different materials. The time domain thermoreflectance laser setup can provide thermal diffusivity and phonon lifetime, which can be correlated with lattice damage. This advanced nonlinear optical technique will enable measurements of nanostructure damage in the metal before and after tritium exposure within a year. This report discusses current efforts in the design and development of the technique and the application to material characterization.
Reconeixement visual: aprenentatge mitjançant rànquings en petits dominis i aprenentatge continu en nous dominis

Deep convolutional neural networks (CNNs) have achieved superior performance in many visual recognition application, such as image classification, detection and segmentation. In this thesis we address two limitations of CNNs. Training deep CNNs requires huge amounts of labeled data, which is expensive and labor intensive to collect. Another limitation is that training CNNs in a continual learning setting is still an open research question. Catastrophic forgetting is very likely when adapting trained models to new environments or new tasks. Therefore, in this thesis, we aim to improve CNNs for applications with limited data and to adapt CNNs continually to new tasks. Self-supervised learning leverages unlabelled data by introducing an auxiliary task for which data is abundantly available. In the first part of the thesis, we show how rankings can be used as a proxy self-supervised task for regression problems. Then we propose an efficient backpropagation technique for Siamese networks which prevents the redundant computation introduced by the multi-branch net- work architecture. In addition, we show that measuring network uncertainty on the self-supervised proxy task is a good measure of informativeness of unlabeled data. This can be used to drive an algorithm for active learning. We then apply our framework on two regression problems: Image Quality Assessment (IQA) and Crowd Counting. For both, we show how to automatically generate ranked image sets from unlabeled data. Our results show that networks trained to regress to the ground truth targets for labeled data and to simultaneously learn to rank unlabeled data obtain significantly better, state-of-the-art results. We further show that active learning using rankings can reduce labeling effort by up to 50% for both IQA and crowd counting. In the second part of the thesis, we propose two approaches to avoiding catas- trophic forgetting in sequential task learning scenarios. The first approach is derived from Elastic Weight Consolidation, which uses a diagonal Fisher Information Matrix (FIM) to measure the importance of the parameters of the network. However the diagonal assumption is unrealistic. Therefore, we approximately diagonalize the FIM using a set of factorized rotation parameters. This leads to significantly better performance on continual learning of sequential tasks. For the second approach, we show that forgetting manifests differently at different layers in the network and iiipropose a hybrid approach where distillation is used in the feature extractor and replay in the classifier via feature generation. Our method addresses the limitations of generative image replay and probability distillation (i.e. learning without forget- ting) and can naturally aggregate new tasks in a single, well-calibrated classifier. Experiments confirm that our proposed approach outperforms the baselines and some start-of-the-art methods.
Likheter som styrkor i webbdesign : Ett alternativ till lokalisering

This study addresses the question of how globalization affects the need for localized user interfaces. Previous research in cross-cultural design suggests that user interfaces need to be adapted to ...
The Modeling of Decomposable Gene Regulatory Network Using US-ELM

In the method of constructing gene regulatory network, the correlation model and Bayesian network model have attracted wide attention of researchers with their respective advantages. However, due to their inevitable disadvantages, the effect of using a single method to constructing gene regulatory networks is not ideal. The combination of these two models may be better to construct gene regulatory network. Therefore, this paper proposed a decomposable gene regulatory network modeling method (DGRN). The DGRN combines three methods including the correlation model, unsupervised extreme learning machine (US-ELM) and Bayesian network model (BN) which used to construct initial network, decompose network, and optimize network structure respectively. Initial network are constructed by correlation model and decomposed by US-ELM. The Bayesian network model is used to optimize the structure of the decomposed gene regulatory subnetworks. The experimental results show that the proposed method can improve the computational efficiency and the scale of the network while ensuring good construction results.
Subject guides: Text and data mining: 5. Text and data mining methods

An introduction to text and data mining concepts and an overview of the steps involved in undertaking text and data mining as part of a research project.