Reviewerly/exHarmony huggingface dataset:
Wearable xAI: A Knowledge-Based Federated Learning Framework

Federated learning is a knowledge transmission and training process that occurs in turn between user models on edge devices and the training model in the central server. Due to privacy policies and concerns and heterogeneous data, this is a widespread requirement in federated learning applications. In this work, we use knowledge-based methods, and in particular case-based reasoning (CBR), to develop a wearable, explainable artificial intelligence (xAI) framework. CBR is a problem-solving AI approach for knowledge representation and manipulation, which considers successful solutions of past conditions that are likely to serve as candidate solutions for a requested problem. It enables federated learning when each user owns not only his/her private data, but also uniquely designed cases. New generated cases can be compared to the knowledge base and the recommendations enable the user to communicate better with the whole system. It improves users’ task performance and increases user acceptability when they need explanations to understand why and how AI algorithms arrive at these optimal solutions.
Enhanced Clustering Approach for Web Usage Mining

Data mining technology has been considered as important means for extracting patterns and trends from huge amount of information. So, this approach is essentially used to extract the unknown pattern from the large set of information for business and also for real time applications. data processing may be a computational intelligence discipline which has emerged as a effective tool for data analysis, new KDD and good higher cognitive process. The raw and unidentified data from the huge volume of dataset may be classified initially in an unsupervised fashion by using cluster analysis.Clustering technique is the process of grouping objects together by which similar objects are combined into one group and disimilar objects into other group. Clustering technique may be employed in many applications for instance biological, financial applications and many more. one of these application types is Web clustering where differing kinds of objects will be clustered into different groups for various purposes. This paper deals with the various aspects of Web data processing and provides a summary about the different techniques employed in this field.
Deepfake Image Detection Using Convolutional Neural Network

The recent advancements in the field of Artificial Intelligence and Machine Learning allow the unethical practice of generation of forged videos by using face swaps and imitating the facial expressions, voice, features and other attributes to serve malicious purposes. The difficulty in distinguishing between the real and the forged videos poses a serious threat to the society, democracy and privacy of a person. The remarkable quality of the forged videos along with the widespread reach through social media platforms manipulates public opinion by influencing their sentiments and perceptions. It is therefore essential to devise ways through which deepfake videos can be detected because otherwise, its repercussions are devastating beyond one’s imagination. This paper firstly provides a comprehensive overview of the various deepfake generation and detection techniques andthenpresents a deepfake detection model using Inception-v3 Architecture.
Anime Sketch Colorization by Component-based Matching using Deep Appearance Features and Graph Representation

Sketch colorization is usually expensive and time-consuming for artists, and automating this process can have many pragmatic applications in the animation, comic book, and video game industry. However, automatic image colorization faces many challenges, because sketches not only lack texture information but also potentially entail complicated objects that require acute coloring. These difficulties usually result in incorrect color assignments that can ruin the aesthetic appeal of the final output. In this paper, we present a novel component-based matching framework that combines deep learned features and quadratic programming with a new cost function to solve this colorization problem. The proposed framework inputs a character's sketches as well as a colored image in the same cut of a movie, and outputs a high-quality sequence of colorized frames based on the color assignment in the reference colored image. To carry out this colorization task, we first utilize a pretrained ResNet-34 model to extract elementary components' features to match certain pairs of components (one component from the sketch and one from reference). Next, a graph representation is constructed in order to process and match the remaining components that could not be done in the first step. Since the first step has reduced the number of components to be matched by the graph, we can solve this graph problem in a short computing time even when there are hundreds of different components present in each sketch. We demonstrate the effectiveness of the proposed solution by conducting comprehensive experiments and producing aesthetically pleasing results. To the best of our knowledge, our framework is the first work that combines deep learning extraction and graph representation to colorize anime sketches and achieves a high pixel-level accuracy at a reasonable time cost.
PEAK DEBIT ANALYSIS USING SYNTHETIC UNIT HYDOGRAPHIC METHOD ITB 1 AND HEC-RAS VERSION 5.0.7 (Case Study of Manna Downstream watershed)

Flooding is a natural disaster caused by the discharge or volume of water that flows in a river exceeds its stream capacity. One of the causes of the flood is high rainfall. The discharge of a river flow depends on the rainfall that falls in a watershed. The purpose of this study was to determine the peak discharge due to the intensity of rainfall in the downstream of Air Manna Watershed and determine the point that will undergo runoff on the Air Manna river flow. This study used Synthetic Unit Hydrograph (HSS) ITB 1 method and analyzed used HEC-RAS 5.0.7. From the result of hydrological analysis used the HSS ITB 1 method, it was found out the peak discharge in the downstream of Air Manna Watershed for return periods 2, 5, 10, 25, 50 and 100 years was 1322.21 m3/s, 1492.94 m3 /s, 1594 12 m3 /s, 1712.20 m3/s, 1794.33 m3 /s, and 1872.85 m3/s. After analyzing used HEC-RAS 5.0.7 software, Air Manna river was unable to accommodate the flow rate that occurred and undergo runoff along the flow.
Processed HIV prognostic dataset for control experiments

Abstract This paper provides a control dataset of processed prognostic indicators for analysing drug resistance in patients on antiretroviral therapy (ART). The dataset was locally sourced from health facilities in Akwa Ibom State of Nigeria, West Africa and contains 14 attributes with 1506 unique records filtered from 3168 individual treatment change episodes (TCEs). These attributes include sex, before and follow-up CD4 counts (BCD4, FCD4), before and follow-up viral load (BRNA, FRNA), drug type/combination (DTYPE), before and follow-up body weight (Bwt, Fwt), patient response to ART (PR), and classification targets (C1-C5). Five (5) output membership grades of a fuzzy inference system ranging from very high interaction to no interaction were constructed to model the influence of adverse drug reaction (ADR) and subsequently derive the PR attribute (a non-fuzzy variable). The PR attribute membership clusters derived from a universe of discourse table were then used to label the classification targets as follows: C1=no interaction, C2=very low interaction, C3=low interaction, C4=high interaction, and C5=very high interaction. The classification targets are useful for building classification models and for detecting patients with ADR. This data can be exploited for the development of expert systems, for useful decision support to treatment failure classification [1] and effectual drug regimen prescription.
LibGuides: Test Preparation Materials: Advanced Placement (AP)

A guide to the test preparation materials available in the OHS Library, Frederick County Public Libraries, and online.
KLASIFIKASI KESALAHAN EJAAN KATA PADA ABSTRAK ARTIKEL ILMIAH MENGGUNAKAN METODE LEVENSHTEIN DISTANCE

The purpose of this research is to make a spell checker application model for abstract documents of journal using the Levenshtein Distance calculation method. Levenshtein Distance is a text mining method used to correct the wrong words. Before calculating Levenshtein Distance, the first step to do is preprocessing text to reduce the number of dimensions of the matrix to make it easier when used as input parameters into the Levenshtein Distance method, which includes tokenizing and stemming. Then calculate between the 2 strings to get the value of the edit distance used to edit (insertion, deletion, substitution). The Levenshtein Distance performance test results using the Confusion Matrix method so that the accuracy value is 73.70%, the precision value is 25.89% and the recall value is 37.27%. Based on the presentation, the conclusion is the Levenshtein Distance method can be used as a spell checker of words.
The Challenges of New Educations for University Students in Covid-19 Outbreak

Introduction: Virtual or online education is one of the common methods in different countries of the world, something that can be more used in different and special situations or conditions such as, we are today confronting with, Covid-19 pandemic. It is faced today with different challenges mostly in education for higher education students. We surveyed the challenges of new educations for university students in covid-19 outbreak. Methods: To conduct the survey research we studied the related text with the subject in the web and asked some students views and thoughts on new education, e-learning, online or virtual education conducted today to give the challenges a fast answer that may not completely satisfying the students. A semi-phenomenological like method was used to collect the needed data to be transliterated then for the tables provided. Results: Increasing the facilities, familiarity, cost reduction, fast speed, and access to the Internet has an effective role in the development of this method. The only thought-provoking factor is the way students perform and their views due to the infancy of the method, which needs to be studied and followed up to be more developed and restructured well. Even if it has some different advantages for performing the job well in some areas, but some of the students have no a positive attitude toward it in education as a whole. Conclusion: There are many different views and attitude toward new education performed todays in higher education fields and areas around the planet with positive and negative aspects and understanding. It is the duties of the men who are expert in the field and responsible to use them and pick up the best to present the way or methods that act well and has the most efficiency for all with low cost, fast speed, high accuracy to reach the goal.
Video Enhancement Network Based on Max-Pooling and Hierarchical Feature Fusion

In this paper, we propose an efficient convolution neural network to enhance the quality of video compressed by HEVC standard. The model is composed of a max-pooling module and a hierarchical feature fusion module. The max-pooling module extracts feature from different scales and enlarges the receptive field of the model without stacking too many convolution layers. And the hierarchical feature fusion module accurately aligns features from different scales and fuses them efficiently. Two modules are applied in the proposed network, our model reconstructs compressed video frames with higher visual quality. Besides, the model is constructed in the full convolution network, thus it can adapt to videos in variable resolutions. The experiment results show that the proposed model outperforms existing models in the terms of PSNR under the same dataset.
cyber safety for adolescents

Cyber safety is the safe and accountable use of data and communication technology. It is regarding keeping personal data safe, secure and self-protection from cyber crimes. Adolescents are exposed to various crimes like cyber bullying, cyber grooming, online gaming fraud, email fraud and so on. Adolescents should aware about how the cyber crime happens, occurs through, various forms, how to protect them self and how to report in case of you are victim.
Research Guides: Introduction to Text Mining: Subsetting with Stringr

An overview of text mining tools and techniques. Learn how to use Stringr functions to subset strings in R
The art of conversing with a machine: When the designer becomes a dialog writer

The adoption of conversational interfaces like chatbots and personal assistants by the general public has meant that designers are being offered an increasing number of projects. Their expertise is required in several fields such as IT and linguistics, as well as user experience, which is a central part of the work of designers. However, these interfaces seem to depart in several respects from other projects involving interfaces. They are characterized by interactions in the form of short dialogs and thus hold out the promise of more natural interactions with users, as they are developed with human language in mind. We will try to understand how the role of designers in such projects might reveal a partially successful humanization process. Given the dialog-based nature of these interactions, designing also becomes close to the work done by dialog writers. We will explore how fruitful the proximity between designing conversational interfaces and playwriting can be. This will allow us to better answer the main question of this article: To what extent are conversational interfaces a new object for designers, not only with regard to the skills required, but also to the designer-interface-user relationship at work?
LibGuides: Incorporating Sources Ethically: Additional Resources on Paraphrasing, Quoting & Summarizing

How to ethically incorporate sources into your writing by paraphrasing, summarizing, and quoting.
Gantry type rotating platform obstacle avoidance crane

Abstract The rapid development of modern logistics industry has higher requirements for logistics machinery and equipment. As an important equipment for material handling, cranes are used more and more widely in the modern production process. At present, cranes have problems such as large size, inconvenient movement, and inaccurate positioning. Based on the above background, the project team designed a gantry type rotating platform obstacle avoidance crane. The crane breaks through the transportation limitations of the original crane. It uses the direct rotation of the pan-tilt and the movement of the spreader on the track to accurately avoid obstacles, which improves the flexibility and accuracy of transportation; the entire transportation process is fully automatic control, which combines the machinery with The combination of intelligence improves transportation efficiency and realizes the accurate transportation of multiple goods.
A Survey on Frameworks and Techniques of Image Mining

In current situation, image plays vibrant role in all part of business such as medical images, satellite images, and business images and so on. Image mining is along branch of data mining that is fretful with the process of knowledge discovery concerning digital images. Image mining is interesting field which covers traditional data mining from structured data to unstructured data such as image data. This growth in number of images and image databases has required the need for image mining techniques. Image mining is a session of analytical techniques that examine a large amount of image data. The main objective of this paper is to present a survey of the different categories of frameworks used to illustrate image mining methods and techniques utilized for image mining applications.
Trace element variation in ore minerals from the Iron Cap deposit (KSM), British Columbia, Canada: Implications for fluid evolution in porphyry-epithermal gold systems

Trace amounts of elements such as As, Sb,Se, Te, Pd, Hg and Au are commonly identified in porphyry and epithermal mineral deposits. Investigating variations in the concentration, distribution and hosting of these elements in an ore mineral suite has the potential to enable elucidation of the specific characteristics of discrete fluids throughout the evolution of a mineralizing magmatic-hydrothermal system. The Iron Cap deposit in the Kerr-Sulphurets-Mitchell (KSM) district, British Columbia, Canada, provides an opportunity to interpret the characteristics of hydrothermal fluids from an early porphyry stage, through transition, to a late epithermal stage. A suite of 60 core samples are characterized by petrographic methods to constrain the sequence of vein formation at Iron Cap, and identify deposit mineralogy. The results of initial trace element analysis of ore minerals by SEM-EDX analysis has shown that pyrite growth zones are Cu-or As-bearing, and arsenopyrite growth zones have a variable Fe:As:S ratio. Galena commonly contains Se, ranging in concentration from ~2wt. % to clausthalite (PbSe). Additionally, gold in the deposit contains between 2-42 wt. % Ag. These initial discoveries will allow a methodology to be developed to interpret fluid conditions with trace element variations.
Loma Linda University Libraries: Mobile Printing: Add Funds Online

Details the various methods available for printing from mobile devices. Instructions for adding funds via the LLU Print Center via PayPal.
ZZ freedom in two qubit gates

Superconducting qubits on a circuit exhibit an always-on state-dependent phase error. This error is due to sub-MHz parasitic interaction that repels computational levels from non-computational ones. We study a general theory to evaluate the `static' repulsion between seemingly idle qubits as well as the `dynamical' repulsion between entangled qubits under microwave driving gate. By combining qubits of either the same or opposite anharmonicity signs we find the characteristics of static and dynamical ZZ freedoms. The latter universally eliminate the parasitic repulsion, leading us to mitigate high fidelity gate operation. Our theory introduces new opportunities for making perfect entangled and unentangled states which is extremely useful for quantum technology.
Mission Impossible: COVID-Less Movie Theater

Author(s): Henderson, Colby; Khanna, Dhruv; Vasa, Jainam; Kumar, Manjari; Kim, Sang Hee | Editor(s): Khalil, Myriam | Abstract: In today’s world, the COVID-19 pandemic has affected many lives and forced the people to take safety precautions like never before. Therefore, designing an environmentally friendly theatre minimizing germ propagation using filtration mechanisms is a valuable project, as it would help struggling businesses, and give people a chance to enjoy a day outside, safely. With safety being the highest priority, the incorporation filtration systems and contactless food delivery networks is essential while providing a fun and lasting memory for the visiting audience. However, to meet the previously listed goals, solutions to major obstacles are necessary. This includes designing a robot with consistent movements while simultaneously avoiding its surrounding obstacles, building an energy environment friendly cooling system, and creating a filtration mechanism to filter the COVID-19 particles. With these overall designs incorporated into the traditional movie theatre, the team presents the “COVID-less Movie Theatre”.
Quality assurance of teaching and learning process in economic higher education under the pandemic

Long-lasting pandemic uncovered the new challenge of tertiary education: how to ensure the high quality of durable emergency distance learning and enable the students to master the necessary competences. The objective of this research was to test the application of the bottom-up approach to quality assurance when students, as stakeholders, are enabled to influence content and context of their learning through regular surveys. Current research represents the comparative analysis of two surveys, conducted at the end of spring semester of 2019-2020 academic year (593 respondents), and at the end of autumn semester of 2020-2021 academic year (1193 respondents) among the students of the Faculty of Economics of Taras Shevchenko National University of Kyiv, outlining the problems students faced while the emergency remote learning was conducted during the first lockdown and measuring how these problems were fixed during the second shutdown bearing in mind the results of the first survey. The methodology combined quantitative and qualitative research methods. The current research covers the study of the methods of teaching, the level of communication between learners and educators, the level of practical skills acquisition, engagement of students in the learning process during two semesters. Also it analyses the measures undertaken by the teachers and faculty management to achieve improvement in learners’ feedback during the second survey that can be considered as best practices.
Efficient Video Super-Resolution Using Deep Convolutional Autoencoders

Video super-resolution is the most well-known area of research in computer science. A video super-resolution technique is commonly required to recreate high-resolution video from noisy, blurry, and low-resolution video. Super-resolution is used in many applications like biomedical image processing, computer vision, and satellite image processing. This paper proposes a deep convolution auto-encoder-based video super-resolution model, which is trained with high-resolution video frames. An autoencoder is an unsupervised neural network that learns how to minimize data through design and reconstructs the loss of as little data as possible. In the test model, low-resolution frames are extracted from the low-resolution video. These low-resolution frames are then passed to the proposed architecture, which is modeled using a convolution auto-encoder. Important features of frames are extracted using multiple convolutional layers and different filters in the encoder model. High-resolution frames are reconstructed using decoder by minimizing loss function using L1 regularization with backpropagation, and weight matrices are updated with Adam optimizer. The proposed model’s efficiency is evaluated and contrasted with state-of-the-art PSNR, SSIM, BRISQUE, VIFP, and UQI techniques. The proposed autoencoder model shows excellent performance.
딥러닝 언어모델의 한국어 학습자 말뭉치 원어민성 판단 결과 분석 연구

The present study aims to analyze how deep learning judges nativelikeness in the corpus of native Korean speakers and Korean learners. To this end, a deep learning model that classifies sentences of native Korean speakers and Korean learners was built, and the criteria for determining nativelikeness between deep learning and humans were compared and analyzed in terms of error analysis. As a result of the analysis, the accuracy of the deep learning model built in this study was found to be 91%, which means that 91 sentences out of 100 sentences were accurately classified whether they were written by the native speaker or by the learner. In addition, since the error annotation result of the learner corpus is a projected result of human judgment of nativelikeness, the similarities and differences of the criteria for determining nativelikeness were described in detail by comparing it with the test data verification result of deep learning. The results of this study will be an important basis for clarifying what the nativelikeness of native Korean speakers is and for objectively judging the nativelikeness of the language produced by Korean learners.(Yonsei University)