Reviewerly/exHarmony huggingface dataset:
Super Pixel Segmentation with Neuro-Fuzzy Filtering based Complex Impulse Noise Removal for Color Images

In image processing, noise filtering or removal is the most essential task to eliminate the corrupted image pixels from a given image and restore the noiseless image. Many filtering techniques with decision mechanisms have been developed to remove the impulse noise from the images. Among those techniques, a super pixel was detected by segmenting the given color image by using mean shift filtering followed by a clustering based on quaternion color distance. The detected super pixel characteristics were analyzed to classify those as noise-free or noisy or single-point impulse ones. After that, those were eliminated based on a Selected Recursive Vector Median Filter (SRVMF) with adaptive window sizes. However, the performance of this technique depends on the orientation of the input image. Therefore in this article, Super Pixel Segmentation (SPS) with Neuro-Fuzzy network based Filtering (NFF) technique is proposed for color images. Initially, the image is segmented to detect the super pixel followed by classification process based on the analysis of super pixel characteristics in order to classify the super pixel as noise-free, noisy and single-point impulse ones. After that, NFF with hybrid learning rule is applied on the detected noisy pixels to remove those from the images with reduced computational complexity and running time. Finally, the experimental results demonstrate that the proposed technique achieves a better denoising effect and performance compared to the other color image denoising techniques.
The Cycle of Disparities in Economic Outcomes and Opportunities

Loretta J. Mester, President and Chief Executive Officer, Federal Reserve Bank of Cleveland - Swarthmore College – Swarthmore, PA - (via videoconference) – April 15, 2021
Acceptance and Effectiveness of Distance Learning in Public Education in Saudi Arabia During Covid19 Pandemic: Perspectives from Students, Teachers and Parents

The movement control order and shutting down educational institution in Saudi Arabia has jeopardized the teaching and learning process Education was shifted to distance learning in order to avoid any academic loss In the middle of the Covid-19 crisis, there is a need to assess the full image of e-learning in Saudi Arabia To investigate student and teachers' perception and acceptance, parents? attitudes and believes about distance education are the main goals of the study The mix-method research design was employed to collect data Three surveys were distributed to 100 students and 50 teachers and 50 parents from different educational institutions in Saudi Arabia, while semi-structured interviews were conducted with 10 parents Random stratified and convenient sampling methods were adopted Both descriptive and content analysis was conducted using SPSS25 0 and NVIVO software for quantitative and qualitative data accordingly The findings showed that students are comfortable with remote education and are receiving enough support from schools and instructors but they think online education can't replace conventional face-to-face learning Moreover, the results showed that teachers are having challenges in preparing online classes because of the development of conducting online classes and the lack of training However, parents showed negative attitudes regarding the benefits and values of remote education and preferred conventional learning styles in elementary schools Parents tended to reject and resist distance learning for several reasons: professional knowledge and lack of time to support their young kids in online classes, the shortcomings of e-learning, young children's inadequate self-regulation Saudi parents are neither trained nor ready to use e-learning The study provided suggestion and implications for teacher education and policymakers
TEXT RECOGNITION FROM IMAGES USING IMAGE PROCESSING TECHNIQUE

— Image Processing is a next level character and object recognizer. At present image processing is a major technology that evolved in order to identify things as same as human eyes. It is developed by using a trained model in order to capture objects optically, it has greater error rate in order to capture the character and objects. Nowadays it is popularly used everywhere in various businesses and software tools. A paper document is best source of raw data but filtering required information from it is difficult. In order to gather in such a way, we need information into textual document which easily underpass into filters and produces required output from it. Image processing algorithms are not accurate enough to give efficient output of the text document. It can be improved by using trained model which frequently updates the algorithm efficiency and in order to decrease the word error rate. In this way text can be recognized efficiently from the image. The major distractors in this are image quality, image contrast, pixel level impurities and image backgrounds acts a lot in text recognition. Various improvements are brought in order to get the best out of the image content.
Artificial Neural Network Model with PSO as a Learning Method to Predict Movement of the Rupiah Exchange Rate against the US Dollar

The movement of currency exchange rate can be predicted in the next few days, this is used by economic actors to get profit. Artificial Neural Network with the backpropagation learning method is good enough to use for forecasting time series data, it's just that in its application this method was considered to have shortcomings such as a long training time to achieve convergence. The purpose of this research is to form a Multilayer Perceptron Artificial Neural Network model with the Particle Swarm Optimization (PSO) algorithm as a learning method in the case of currency exchange rate prediction. This research produced a model that can predict the movement of the Rupiah exchange rate against the US Dollar, while the model formed was the MLP-PSO model with an error rate of 5.6168 x 10-8, slightly better than the MLP-BP model with an error rate of 6.4683 x 10-8. These results indicated that the PSO algorithm can be used as a learning algorithm in the Multilayer Perceptron Artificial Neural Network.
Chasing Puppies: Mobile Beacon Routing on Closed Curves

We solve an open problem posed by Michael Biro at CCCG 2013 that was inspired by his and others’ work on beacon-based routing. Consider a human and a puppy on a simple closed curve in the plane. The human can walk along the curve at bounded speed and change direction as desired. The puppy runs with unbounded speed along the curve as long as the Euclidean straight-line distance to the human is decreasing, so that it is always at a point on the curve where the distance is locally minimal. Assuming that the curve is smooth (with some mild genericity constraints) or a simple polygon, we prove that the human can always catch the puppy in finite time.
Smart screen system based on LED homogeneous integration effect

A future-oriented smart screen system based on the homogeneous integration effect of LED itself is implemented by integrating the display and the detection, and a 4*4 MQW diode array is developed Combined with self-designed java-based software control interface, thus going to design the future military and civilian smart screen is designed, the integration of detection lighting and display is realized In the display mode, a 16-pixel self-written display block can be implemented to display specific characters or images In the detection mode, the size and the distance of objects in front of the screen can be detected without contact The module can be infinitely expanded The current COVID-19 virus is raging, and the risk of cross-contamination in public places, such as elevators and vehicles, is greatly increased The system does not require contact, thus eliminating the needs for manual operation and reducing the risk of cross-infection © 2021, Periodical Office of Nanjing University of Posts and Telecommunications All right reserved
Library Guides: A Faculty Guide to Kanopy Streaming Video: Link to Licensed Kanopy Videos

How faculty can find, request, and link to Kanopy streaming videos, and also check license expiration dates.
Peningkatan Kemampuan Bahasa Pada Anak Usia Dini Usia 5-6 Tahun

Language is a communication tool used by someone to be able to interact with other people. Langguage skills are very important for children because language is very influential in the growth and development of children as adults. Not only in terms of communication, the advantages of language are also beneficial for other developments. Purpose: This study aims to examine the literature, articles and research documents that identify the increase in languageSkills in children age 5-6 years. Methods: this study is a qualitative study with a literature review method based on reviews of national journals and international journals. The sampling technique used is a reference in the form of primary articles of lass than 10, 7 national journals with a period of 7 years and 2 international journals with a period of 7 years that are quality accredited and reputable with keyword improving language, language, early childhood. The results of the study: show that language improvement in children can be done through sharing ways such as telling stories, conversing, storytelling, oicture and picture learning models and can be influenced by parenting styles. It was found that the selection of appropriate media, methods and application of learning models can improve children’s language skills. Varied and creative media can increase children’s interest in learning so that children are not easily bored.
A Novel Hybrid Approach of Suppression and Randomization for Privacy Preserving Data Mining

In the era of technology advancement, knowledge extraction from large amount of data is very much important task. The process of data mining is applied to get the useful information from the data stored in a centralized server for important decision making process of multiple organizations. When multiple organizations collect the data for mutual gain, it gets vulnerable to individuals private data. Different approaches such generalization, perturbation, cryptography and randomization are used for taking care of the confidentiality of any individuals private data. Each of these methods has their own pros and cons like in anonymization, huge loss of information can take place. Data that is used in the process of data mining contain many attributes which hold confidential data of an individual and many attributes can reveal the private information of an individual, if those are associated with each other. These attributes are called quasi identifiers (QID). Individually these attributes dont breach the security but in a combined way these may be vulnerable towards the security of private data. Thus, there is the requirement of an approach to overcome the problem of disclosing of private data through quasi identifiers. Our proposed method of combining the suppression and randomization presents the solution to this problem. The method conserves the data privacy with the zero information loss in the process of regaining the actual values. The proposed work is carried out by making a local centralized server and outcomes are matched up with anonymization process to obtain the better results.
Cortesia e amorevolezza nei Promessi Sposi 1840

The essay examines rapprochement politeness in two characters of the Promessi Sposi, Federigo Borromeo, in the encounter with the unnamed (chap. XXIII), and Bortolo, in the dialogue with Renzo (ch. XVII). The passages, which contain, in the narrator’s metapragmatic comments, the adjectives affettuoso and amorevole ‘loving’, are analysed with an interdisciplinary method combining the pragmatics of politeness with affective rhetorical figures found in the treatises used in the schools attended by Manzoni.
The Correlation between Students’ Skill of Reading Aloud and Reading Comprehension of the Eleventh Year Students of SMK N 2 Salatiga in the Academic Year Of 2019/2020.

The objective of this study are: (1) To find the profile of students’s levels of student reading aloud for the eleventh year students SMK N 2 Salatiga in the academic year of 2019/2020. (2) To find out the profile of students’ levels of students’ reading comprehension for the eleventh year students of SMK N 2 Salatiga in the academic year of 2019/2020. (3) To find out if there is any correlation between the students’ reading aloud and reading comprehension of the eleventh year SMK N 2 Salatiga in the academic year of 2019/2020.The design of this research was correlational. The subject of this research is XI BKP B, which consist of 34 students. This research used test as the instrument of data collection.The result of this researcher are: (1) Based on the scores of the test given to the students, it can be stated that the students’ reading aloud of the eleventh grade student of SMK N 2 Salatiga in the academic year of 2019/2020 is on high level. (2) Based on the scores of the test given to the students, it can be stated that the students’ reading comprehension of the eleventh grade students of SMK N 2 Salatiga in the academic year 2019/2020 is on good levels. (3) From the result of the statistical calculation, it can be synthesized that the alternative hypothesis (Ha) is accepted. The calculation shows that the coefficient of correlation (rxy) between the variable is 0,5328 which is higher than r table (0,532 > 0.329). The conribution of reading aloud towards reading comprehension of the eleventh grade student of SMK N 2 Salatiga in the academic year 2019/2020 is 23,78% while the 76,22% factors are influenced by other factor. Keywords: Reading Aloud, Reading Comprehension
Analysis of the Potentials of Blockchain for the Governance of Global Commons. Frontiers in Blockchain

In recent years, the increasing need for global coordination has attracted interest in the governance of global-scale commons. In the current context we observe how online applications are ubiquitous, and how emerging technologies enable new capabilities while reshaping sectors. Thus, it is pertinent to ask: could blockchain technologies facilitate the extension and scaling up of cooperative practices and commons management in this global context? In order to address this question, we propose a focus on the most paradigmatic and widely successful examples of global cooperation: non-rival global commons. Examples of these are the digital resources maintained by large peer production communities, such as free/libre open source software and Wikipedia. Thus, this article identifies and analyses the potentialities of blockchain to support the sustainability and management of non-rival global commons. Our approach draws on Elinor Ostrom’s classic principles for commons governance, although revisiting and adapting these to the more challenging scope of global commons. Thus, in this work we identify the affordances which blockchain provides (e.g. tokenisation, formalisation of rules, transparency or codification of trust) to support the effective management of this type of global commons based on these adapted Ostrom principles. As part of our analysis, we provide numerous examples of existing blockchain projects using affordances in line with each principle, as well as potential integrations of such affordances in existing practices of CBPP communities. Our analysis shows that, when considering the challenges of managing global commons (e.g. heterogeneity or scale), the potential of blockchain is particularly valuable to explore solutions that: distribute power, facilitate coordination, scale up governance, visibilise traditionally invisible work, monitor and track compliance with rules, define collective agreements, and enable cooperation across communities. These affordances and the subsequent analysis contribute to the emergent debate on blockchain-based forms of governance, first by providing analytical categories for further research, but also by providing a guide for experimentation with the development of blockchain tools to facilitate global cooperation.
Check Raising Strategy | Learning Poker Strategy with Tadas Peckaitis

When it comes to Texas Hold’em, there are various strategies one can employ both before and after the flop to surprise your opponents and catch them ...
Efficient Eye Blink Detection Method for the Disabled

Abstract: There are a couple of clinical issues that can incite an individual getting weakened or having engine discourse issues that hinders discourse or voice creation. Conditions, for instance, Motor neuron illnesses, for instance, Amyotrophic Lateral Sclerosis (ALS) and Cerebral Palsy are among the normal diseases that impact talk. In all or most such cases, the patient loses the ability to talk with the rest of the world in a feasible manner regardless of the way that his understanding is commonly unaffected. Some revamp Augmentative and Alternative Communication (AAC) devices have been developed that uses signals from the patient and changes over them into some kind of data that can be imparted however such gadgets are extravagant and are for all intents and purposes unattainable for the vast majority influenced. This venture expects to give a very low-estimated gadget that peruses and changes over eye-flickers from the patient to an all-around acknowledged correspondence code-The Morse code. The application is utilized continuously for examining the impact of light and distance between the eyes and the cell phone to assess the exactness location and generally speaking precision of the framework. Test outcomes show that our proposed strategy gives a 90% by and large exactness and 100% recognition precision for a distance of 15 cm and a counterfeit light.
MACHINE LEARNING-BASED WEATHERFORECASTING ON FREELY AVAILABLE WEATHER DATA

Weather forecasting is mostly a numerical measurerather than a binary conclusion. Precise weather forecasting is one of today’s highlyinteresting tasks which deals with anenormousamount of observations and features. We aimto develop an intellectual weather forecasting module. Asmartforecast based on the freely available data is achievedwith machine learning techniques. This module reflects trails such as maximum temperature, minimum temperature, and rainfall for an experimented period of years, and they are evaluated. The dataset is repossessed from a website named DarkSky.To abridge the recovery of data,a Python API is used to studymeteorological data has been established. The study is grounded on Linear Regression and anArtificial Neural Network model thatforecast next day weather with goodprecision. Anaccurateness of more than 94% is gained based on the dataset. Newexplorations haveshown that ML methods accomplishedbetter presentation than old-style statistical approaches. Machine learning, a subdivision of artificial intelligence has establishedto be a robust method offoreseeing and examining a given data set. This unit plays asignificantpart in the field of agriculture where weather forecasting is a vital aspect.
A Framework for I-Voting based on Helios and Public-Key Certificates

With the development of technology, so-called electronic voting has emerged. Of particular interest to us is Internet voting (I-voting) because this method contributes to raising the percentage of voters as they can vote from anywhere and from any device, thus providing them with comfort. Indeed, the speed of counting is quick and results can be announced within a short period of time. Many voting systems have appeared in recent years but the most famous was Helios because it is open-source, online available, and anyone can use it to create elections. It is a powerful and reliable system and has many features but also contains some limitations such as it is used in low coercion elections only.In this paper, a general framework for I-voting based on the Helios voting system and public key certificates is proposed. In this framework, Helios is suggested to be linked with a certification authority to provide the necessary keys for digital signature and vote encryption. Furthermore, each voter is suggested to have the possibility to have more than one voting account, one of which is a real voting account. The others are fake accounts that might be used by the voter in case of coercion. If a voter uses the fake account to vote, her/his vote will not be counted in the final votes. We also improve the Helios interface by simplifying it, adding more explanation to each step used in voting, and adding an Arabic interface so that Arab people can use the system more easily.
LibGuides: Library Orientation: Referencing Video

The purpose of this guide is to introduce you to services and resources available at your Library.
DDPG Agent to Swing Up and Balance Cart- Pole System

Reinforcement learning is a mathematical framework for agents to interact intelligently with their environment. Unlike supervised learning, where a system learns with the help of labeled data, reinforcement learning agents learn how to act by trial and error only receiving a reward signal from their environments. A field where reinforcement learning has been prominently successful is robotics [3]. However, real-world control problems are also particularly challenging because of the noise and high- dimensionality of input data (e.g., visual input). In recent years, in the field of supervised learning, deep neural networks have been successfully used to extract meaning from this kind of data. Building on these advances, deep reinforcement learning was used to solve complex problems like Atari games and Go. Mnih et al. [1] built a system with fixed hyper parameters able to learn to play 49 different Atari games only from raw pixel inputs. However, in order to apply the same methods to real-world control problems, deep reinforcement learning has to be able to deal with continuous action spaces. Discretizing continuous action spaces would scale poorly, since the number of discrete actions grows exponentially with the dimensionality of the action. Furthermore, having a parametrized policy can be advantageous because it can generalize in the action space. Therefore with this thesis we study state-of-the-art deep reinforcement learning algorithm, Deep Deterministic Policy Gradients. We provide a theoretical comparison to other popular methods, an evaluation of its performance, identify its limitations and investigate future directions of research. The remainder of the thesis is organized as follows. We start by introducing the field of interest, machine learning, focusing our attention of deep learning and reinforcement learning. We continue by describing in details the two main algorithms, core of this study, namely Deep Q-Network (DQN) and Deep Deterministic Policy Gradients (DDPG). We then provide implementatory details of DDPG and our test environment, followed by a description of benchmark test cases. Finally, we discuss the results of our evaluation, identifying limitations of the current approach and proposing future avenues of research.
Automatic Difficulty Classification of Arabic Sentences

In this paper, we present a Modern Standard Arabic (MSA) Sentence difficulty classifier, which predicts the difficulty of sentences for language learners using either the CEFR proficiency levels or the binary classification as simple or complex. We compare the use of sentence embeddings of different kinds (fastText, mBERT , XLM-R and Arabic-BERT), as well as traditional language features such as POS tags, dependency trees, readability scores and frequency lists for language learners. Our best results have been achieved using fined-tuned Arabic-BERT. The accuracy of our 3-way CEFR classification is F-1 of 0.80 and 0.75 for Arabic-Bert and XLM-R classification respectively and 0.71 Spearman correlation for regression. Our binary difficulty classifier reaches F-1 0.94 and F-1 0.98 for sentence-pair semantic similarity classifier.
Research Guides: EDU 630 - Strategies for Online Instruction: Growth Mindset & Mastery Based Learning

Research guide developed specifically to support the EDU-630 learning objectives & projects.