Reviewerly/exHarmony huggingface dataset:
Reservoir computing system based on an optoelectronic oscillator with double delay feedback

A time-delayed reservoir computing (RC) system based on an optoelectronic oscillator with double delay feedback is proposed. On the basis of a conventional RC system based on an optoelectronic oscillator, an optical delay feedback path is introduced, through which the output light of the system is fed back to the Mach-Zehnder modulator (MZM). A word error rate of 0 for signal waveform classification task and a normalized mean square error of 0.0097 for Santa Fe chaotic time series prediction task are obtained. The simulation results verify the prominent classification and prediction performances of the proposed RC system. The system is compact in structure and easy to implement.
Matrix Models

This chapter discusses the major types of matrix models, a rich and multifarious family of distributional semantic models (DSMs) that extend and generalize the vector space model in information retrieval from which they derive the use of co-occurrence matrices to represent distributional information. We first focus on a group of matrix DSMs (e.g., Latent Semantic Analysis) that we refer to as classical models, since they directly implement the basic procedure to build distributional representations introduced in Chapter 2. Then, we present DSMs that propose extensions and variants to classical ones. Latent Relational Analysis uses pairs of lexical items as targets to measure the semantic similarity of the relations between them. Distributional Memory represents distributional data with a high-order tensor, from which different types of co-occurrence matrices are derived to address various semantic tasks. Topic Models and GloVe introduce new approaches to reduce the dimensionality of the co-occurrence matrix, respectively based on probabilistic inference and a method strongly inspired by neural DSMs.
Distributional Representations

The distributional representation of a lexical item is typically a vector representing its co-occurrences with linguistic contexts. This chapter introduces the basic notions to construct distributional semantic representations from corpora. We present (i) the major types of linguistic contexts used to characterize the distributional properties of lexical items (e.g., window-based and syntactic collocates and documents) , (ii) their representation with co-occurrence matrices, whose rows are labeled with lexemes and columns with contexts, (iii) mathematical methods to weight the importance of contexts (e.g., Pointwise Mutual Information and entropy), ( iv) the distinction between high-dimensional explicit vectors and low-dimensional embeddings with latent dimensions, (v) dimensionality reduction methods to generate embeddings from the original co-occurrence matrix (e.g., Singular Value Decomposition), and (vi) vector similarity measures (e.g., cosine similarity).
Development Of Props Media “Smiling Clock” For Teaching Speaking Skill

In the seventh grade of MTs Ma'arif Kawedusan, it was found that the understanding of English material was relatively low, because there weren't enough learning tools available to spark kids' interest in studying English.The purpose of the research is to develop props media Smiling Clock for teaching the speaking of telling time material.. In this learning media development research used the ADDIE method, where in this method researchers carried out as many as five stages. Analysis, design, development, implementation, and evaluation. After going through several processes until the product was finished, the researcher validated it. The first validation, the instrument validation, was based on instrument experts' evaluations, it received a score of 93% with the category "Very Valid". The second validation the media validation, was based on media experts evaluations, it received a score of 81% with the category "Valid". The last validation, the material validation was based on material experts’ evaluation, it received a score of 90% with the criteria "Very Valid". Furthermore, revise the product according to the advice of media experts, products that are ready to be tested on students. Trials on students obtained data from student responses to learning, the percentage of answers from student responses that met the standard of "Very Good" was 91%. By considering the result of the research, it can therefore say that the props media Smiling Clock can be used for effective and efficient learning.
Developing The Snake And Ladder Game As Media For Teaching Vocabulary At Ma Al Khoiriyah

This study aims to develop snakes and ladders game as learning media to teach descriptive text vocabulary at X class students of MA Al Khoiriyah Putukrejo Gondanglegi Malang. The research method used was the research and development (R&amp;D) method with the ADDIE model which consists of five stages, namely analysis, design, development, implementation, and evaluation. The population of this study were 20 students at X class of MA Al Khoiriyah and the sample was taken by using cluster random sampling method. The instruments used in data collection were questionnaires and unstructured interviews. The results showed that the snakes and ladders learning media for descriptive text vocabulary was very valid and effective in increasing students' learning motivation. In this learning media, students are not only asked to answer questions in each box of snakes and ladders boards, but also to make sentences related to the vocabulary that has been translated. After the game was over, students must remember the new vocabulary that has been found and recorded on the blackboard by the teacher. This research is expected to make a positive contribution in the development of effective and innovative learning media to increase student learning motivation.
Semi-supervised Semi-paired Cross-modal Hashing

Large-scale cross-modal hashing has drawn extensive attention due to its attractive efficiency in both storage and retrieval. Existing methods exhibit poor performance when exploiting the semantic correlations implied in unsupervised and unpaired data during training process. To deal with this issue, we propose a novel hashing method, named Semi-supervised Semi-paired Cross-modal Hashing (SSCH). By leveraging a general and flexible two-step scheme, the proposed method can handle the complex training data effectively and efficiently, where both the common semantics and the modality-specific optimal pseudo semantics are well captured. Specifically, the proposed SSCH performs an alignment-free pseudo-labeling process to get strengthened semantic information. Furthermore, hash representations for various data are learned via a label-enhanced strategy, through which the cross-modal correlations are strengthened and preserved with considering efficiency. The semantic-preserving proof of SSCH is given based on statistical analysis. Also, we prove the stability of the proposed time-saving algorithm using properties of Bregman divergence. Experimental results on three benchmark datasets show that SSCH can obtain satisfactory precision and scalability in various scenarios.
A Graphical Model for Fusing Diverse Microbiome Data

This paper develops a Bayesian graphical model for fusing disparate types of count data. The motivating application is the study of bacterial communities from diverse high-dimensional features, in this case, transcripts, collected from different treatments. In such datasets, there are no explicit correspondences between the communities and each corresponds to different factors, making data fusion challenging. We introduce a flexible multinomial-Gaussian generative model for jointly modeling such count data. This latent variable model jointly characterizes the observed data through a common multivariate Gaussian latent space that parameterizes the set of multinomial probabilities of the transcriptome counts. The covariance matrix of the latent variables induces a covariance matrix of co-dependencies between all the transcripts, effectively fusing multiple data sources. We present a computationally scalable variational Expectation-Maximization (EM) algorithm for inferring the latent variables and the parameters of the model. The inferred latent variables provide a common dimensionality reduction for visualizing the data and the inferred parameters provide a predictive posterior distribution. In addition to simulation studies that demonstrate the variational EM procedure, we apply our model to a bacterial microbiome dataset.
DarkHorse: A UDP-based Framework to Improve the Latency of Tor Onion Services

Tor is the most popular anonymous communication overlay network which hides clients’ identities from servers by passing packets through multiple relays. To provide anonymity to both clients and servers, Tor onion services were introduced by increasing the number of relays between a client and a server. Because of the limited bandwidth of Tor relays, large numbers of users, and multiple layers of encryption at relays, onion services suffer from high end-to-end latency and low data transfer rates, which degrade user experiences, making onion services unsuitable for latency-sensitive applications. In this paper, we present a UDP-based framework, called DarkHorse, that improves the end-to-end latency and the data transfer overhead of Tor onion services by exploiting the connectionless nature of UDP. Our evaluation results demonstrate that DarkHorse is up to $3.62\times$ faster than regular TCP-based Tor onion services and reduces the Tor network overhead by up to 47%.
Solutions of the Schrödinger Equation in One Dimension

Many physics textbooks take a traditional approach to the demonstration of mathematical relationships and derivations, presenting them in linear order. However, many physical derivations follow a tree-shaped structure with interconnected steps running in parallel, where numerous individual equations are manipulated and combined to reach a final result. Thus, conventional presentation often leads to derivations being spread over several book pages and linked by formula numbering. This title takes a novel and intuitive approach to introductory quantum mechanics by utilising concept maps to address non-linear structures in key mathematical relationships. Concept maps are structures in a form similar to flowcharts where derivations, concepts, and relations are visualised on one page, supported by concise accompanying text on the opposite page. Perfect as a supporting and guiding tool for undergraduates, this book is designed to aid in the understanding and memorisation of key derivations and mathematical concepts in quantum mechanics.
Building Physics Apparatus for Children

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Thomas B. Greenslade; Building Physics Apparatus for Children. Phys. Teach. 1 September 2023; 61 (6): 458–460. https://doi.org/10.1119/5.0088001 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAmerican Association of Physics TeachersThe Physics Teacher Search Advanced Search |Citation Search
Solution to the May 2023 Challenge: May u B happy*

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Boris Korsunsky; Solution to the May 2023 Challenge: May u B happy*. Phys. Teach. 1 September 2023; 61 (6): B542–B544. https://doi.org/10.1119/10.0020783 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAmerican Association of Physics TeachersThe Physics Teacher Search Advanced Search |Citation Search
ICG: A Machine Learning Benchmark Dataset and Baselines for Inline Code Comments Generation Task

As a fundamental component of software documentation, code comments could help developers comprehend and maintain programs. Several datasets of method header comments have been proposed in previous studies for machine learning-based code comment generation. As part of code comments, inline code comments are also crucial for code understanding activities. However, unlike method header comments written in a standard format and describing the whole method code, inline comments are often written in arbitrary formats by developers due to timelines pressures and different aspects of code snippets in the method are described. Currently, there is no large-scale dataset used for inline comments generation considering these. Hence, this naturally inspires us to explore whether we can construct a dataset to foster machine learning research that not only performs fine-grained noise-cleaning but conducts a taxonomy of inline comments. To this end, we first collect inline comments and code snippets from 8000 Java projects on GitHub. Then, we conduct a manual review to obtain heuristic rules, which could be used to clean the data noise in a fine-grained manner. As a result, we construct a large-scale benchmark dataset named ICG with 5,740,770 pairs of inline comments and code snippets. We then build a comprehensive taxonomy and conduct a statistical and manual analysis to explore the performances of different categories of inline comments, such as helpfulness in code understanding. After that, we provide and compare several baseline models to automatically generate inline comments, such as CodeBERT, to enhance the usability of the benchmark for researchers. The availability of our benchmark and baselines can help develop and validate new inline comment generation methods, which would also further facilitate code understanding activities.
Access Control Systems Based on Blockchain Technology

Now a days the need for decentralized applications is increasing and blockchain provides the foundational technology for creating and developing them. The security and integrity of the blockchain network and its resources are supported by access controls, which is a crucial component of blockchain-based applications. This paper presents a study of various access control mechanisms, including smart contracts, consensus algorithms, and cryptographic protocols, to assess their benefits and limitations in different contexts of applications. The purpose of the paper is to present various access control systems in blockchain-based applications and their applicability and effectiveness in various use case scenarios.
Prediction of Ethereum gas prices using DeepAR and probabilistic forecasting

Ethereum is a major public blockchain. Besides being the second-largest digital currency by market capitalization for its cryptocurrency, the Ether (Ξ), it is also the foundation of Web3 and decentralized applications, or DApps, that are fuelled by Smart Contracts. At the time of this writing, Ethereum still uses Proof of Work (PoW) consensus algorithm to ensure the integrity of the blockchain and to prevent double spend. PoW requires the participation of miners, who are incentivized to assemble blocks of transactions by being rewarded with cryptocurrency paid by transaction originators and by the blockchain network itself via newly minted Ξ. Network fees for transaction submissions are called gas, by analogy to the fuel used by cars, and are negotiable. They are also highly volatile and hence it is critical to predict the direction they are heading into, so that one can time transaction submissions, when feasible. There have been several efforts to predict gas prices, including usage of large Mempools, analysis of committed blocks, and more recent ones using Facebook's Prophet model [Taylor, S. J., & Letham, B. (2017). Forecasting at scale. PeerJ Preprints, 5, e3190v2. https://doi.org/10.7287/peerj.preprints.3190v2]. In this study, we introduce an innovative approach that employs the DeepAR [Salinas, D., Flunkert, V., Gasthaus, J., & Januschowski, T. (2020). Deepar: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting, 36(3), 1181–1191. https://doi.org/10.1016/j.ijforecast.2019.07.001] model, known for its superior forecasting accuracy over conventional methods by virtue of its ability to learn from multiple related time series. This methodology not only offers immediate advantages but also holds promise for ongoing enhancements. We substantiate our claims through empirical testing, utilizing data extracts from the Ethereum blockchain and cryptocurrency price feeds. This document is an extended version of our ICCS 2022 paper on the same topic. In this paper, we dive deeper into the internals of DeepAR forecasting algorithm [Salinas, D., Flunkert, V., Gasthaus, J., & Januschowski, T. (2020). Deepar: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting, 36(3), 1181–1191. https://doi.org/10.1016/j.ijforecast.2019.07.001], analyse the correlation between the on-chain/off-chain sample data, and describe additional experiments that empirically prove our findings and, finally, perform a comparison of our outputs with those from the Prophet [Taylor, S. J., & Letham, B. (2017). Forecasting at scale. PeerJ Preprints, 5, e3190v2. https://doi.org/10.7287/peerj.preprints.3190v2] model.
Literacy and Education Movement to Increase Public Awareness of the Importance of Education in Buntu Bedimbar Village

This PKM activity aims to increase the literacy of children in Buntu Bedimbar village, especially problem solving in story problems, provide socialization about the awareness of the Buntu Bedimbar community of the importance of education for their children, and to increase children's learning motivation around Buntu Bedimbar Village. As for the results obtained from the activity, when carrying out or carrying out activities, there are obstacles and problems, but all of them can be overcome so that every program implemented is well received, and the community also really helps us in running the PkM program. Another result obtained is the increased learning motivation of children around Buntu Bedimbar Village
The Effect of Knowledge Creation Practices on Managerial and Marketing Innovation Through Creativity

Knowledge creation (KC) is viewed as one of the important practices in knowledge management (KM) process. It is argued that the success of the firm is measured, to a great extent, by its level of innovation. In fact, an innovation-oriented perspective contributes to sustainability. Moreover, innovation is usually developed through new knowledge and creative ideas. That’s why; it is relevant to examine the relationship between KC, creativity, and innovation. The first objective of the present paper is to study three direct relationships: The first relation is between KC and innovation (managerial, marketing). The second direct relation is between knowledge creation and creativity. The Third direct relation is between creativity and innovation. The second objective is to analyze the relationship between KC and innovation through the mediation of creativity. Finally, we will compare the results and identify the strongest and significant links useful for practice. The focus is on non-technological innovation because the majority of studies dealt with technological innovation. Added to that, managerial and marketing innovation is supposed to procure an advantage for emergent economies in a post-revolutionary and Covid context. The quantitative approach is adopted upon which 83 valid questionnaires are analyzed using the SEM with Pls3 to test the relationships between the variables. The results show that KC has a positive and significant effect on the two considered types of innovation. In addition, since KC dynamics allow organizations to develop and generate new ideas favorable to innovation, it has a positive effect on creativity. The test of mediation proves that creativity intervenes in the relation between KC, operationalized by the SECI model, and innovation. Based on these results, we may conclude that the relationship between KC and innovation becomes the strongest with the mediation of creativity. Certainly, these results have several implications both on the theoretical and empirical sides.
Predicting The Quality of Red Wine and White Wine Using Data Mining

In business intelligence or artificial intelligence, data mining is a technique that can classify and cluster data based on the nature and correlation of the data set used. in data mining, several methods can be used, such as C45, K-Means, Apriori Decision Tree, KNN, LSTM, Naive Bayesian, etc. This research utilizes the Decision Tree method which aims to classify the quality of red wine and white wine. The results of this study indicate that the prediction of red wine has a precision of 61.1%, recall of 60.7%, f-measure of 60.3%, and an average accuracy of 60.7% while white wine has a precision of 58.2%, recall of 58.7%, f-measure 58.4%, and 58.7% accuracy. The method used in this study also shows that Decision Tree can outperform other methods such as Lib-SVM, BayesNet, and Multi Perceptron.
The Effect of the Contextual Teaching and Learning (CTL) Learning Model Assisted by Learning Video Media on the Economic Learning Outcomes of Class XI IPS Students of SMA Negeri 1 Completed T.A 2022/2023

This study aims to determine differences in students' economic learning outcomes using the Contextual Teaching and Learning (CTL) learning model assisted by learning video media compared to using conventional learning models in class XI IPS at SMA Negeri 1 Completed Academic Year 2022/2023. The population in this study were 141 students in class XI IPS, while the sample in class XI IPS 3 and 4 was 69 students. The results of the research show that the post test significance value for the experimental and control classes is 0.0035 &lt;0.05, so Ho is rejected. This means that there is a significant difference in student economics learning outcomes that are higher by using the Contextual Teaching and Learning (CTL) learning model assisted by learning video media compared to using conventional learning models in class XI IPS SMA Negeri 1 Completed Academic Year 2022/2023. So, it can be concluded that the Contextual Teaching and Learning (CTL) learning model assisted by learning video media has an effect on the economics learning outcomes of class XI IPS students of SMA Negeri 1 Completed T.A 2022/2023.
ARTIFICIAL INTELLIGENCE(AI) APPLICATIONS IN HIGHER EDUCATION: ADDRESSING THE ELEPHANT IN THE ROOM: CASE STUDY ON AI

Purpose:This study helps us to know about the impact of artificial intelligence on higher education .With the help of SWOT analysis , PESTLE analysis and other alternatives for AI. Design:Study information is gathered from the secondary data from Published papers like Research gate, and Google Scholar. The study is done by analysing through SWOT analysis, brand bullseye, and competitors of Britannia company. Findings:In this study we learnt that AI remains inhuman .The chances to outsmart are high.Till the date government have not implemented AI in the workings of the Government.It is beneficial if we go on implementing it in the automobile industry .It also improves the working of the employees ,reduces the workload,work life balance etc. It also increase the workplace productivity Originality:This study helps us to know about Artificial Intelligence applications in higher education. Paper type:Case study KEYWORDS: artificial intelligence , education, SWOT analysis ,PESTLE analysis ,competitors