Reviewerly/exHarmony huggingface dataset:
Motivasi Belajar Mempengaruhi terhadap Hasil Belajar Siswa Kelas V di Sekolah Dasar

The purpose of this study was to determine how much motivation to learn affects the learning outcomes of fifth grade students in elementary schools 064021 Medan City. The method used is a quantitative correlational method with data collection techniques by means of library research and field research through interviews, observations and documentation. With the results of the calculation of the correlation number of variable X (learning motivation) to the variable Y (learning outcomes) of 0.965. This means that the effect of learning motivation on student learning outcomes in grade V SDN 064021 Medan Helvetia Medan city is very high.
Organization and Some Results of the Work Baikal Ferry in 1904–1905

For many years, the Russian-Japanese war was considered ingloriously lost in Russian historiography. The world and domestic liberal press, and then the Soviet historical science, tried with all their might to create the impression of a complete defeat of Russia. However, if we conduct a deeper analysis of military events, it becomes obvious that only a year after the start of the war, Japan’s offensive pressure dried up and its emerging economic and military problems came to the fore. Japan did not expect that the enemy would be able to establish an effective and uninterrupted supply of troops: in a short time, instead of a poorly equipped Siberian road, a well-established highway was developed and operated, connecting the center of Russia with the army fighting in the east of the country by a continuous rail route. The Baikal ferry in 1904–1905, being the only link of the Trans-Siberian Railway, determined the capacity of the entire highway. This article highlights some aspects and results of its work during this period.
Wallstopia : Pathways to the Real-world Providing Augmented Reality (AR) Social Platform for Individuals to Exhibit Their Medium in Real-world

Most people are living in a fully connected world where high-speed Internet access is becoming the standard. The time is at hand where people can access any information with a simple click on almost any connected device. What is the next step in this technological development? What is the innovation that will become part of everyday human life? There is growing evidence that Augmented Reality (AR) indeed is to be part of the future. This new technology has the potential to alter the way individuals interact with the real-world significantly. AR may become the leading user-interface metaphor for situated computing. It has the unique quality of providing a direct link between the physical world and virtual information about that reality. For this reason, the purpose of Wallstopia AR application is to engage users in experiencing the real-world by using augmented reality (AR) to create, share, and exchange their media while interacting with the real-world. The Wallstopia iPhone application is designed to keep a user within the Wallstopia environment and allow them to generate different types of content by using a different strategy potentially involving real walls, visual walls, and virtual or real rooms that the application provides in order to exhibit their media to other users. Wallstopia includes interaction that adds value to users’ experience such as a selection of viewing angles, allowing movement inside a room, geolocation, touching, and sharing of walls (rooms and visual walls) with other users.
High Quality Color Image Compression for Discrete Transform Domain Downward Conversion Block Based Image Coding

Text and image data are important elements for information processing almost in all the computer applications. Uncompressed image or text data require high transmission bandwidth and significant storage capacity. Designing and compression scheme is more critical with the recent growth of computer applications. Among the various spatial domain image compression techniques, multi-level Block partition Coding (ML-BTC) is one of the best methods which has the least computational complexity. The parameters such as Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) are measured and it is found that the implemented methods of BTC are superior to the traditional BTC. This paves the way for a nearly error free and compressed transmission of the images through the communication channel.
Enhanced Weight Based Convolutional Neural Network (EWCNN) and Fuzzy Clustering For Semantically Rich Multi-Label Social Emotion Classification

In Recent Years, Social Emotion In Recent Years Acquires Natural Language Processing Researchers’ Attention, Because Of Analyzing User-Generated Emotional Documents On The Web. But, These Emotions Has Noisy Instance Mixed And It Is Great Dispute To Acquire The Textual Meaning Of Short Messages. Definition: In General, Large-Scale Datasets Will Have Many Noisy Data, Which Can’t Be Used Readily And Also It Is Costly, Because Of Ambiguity Of Various Informal Expressions In User-Generated Comments. It Is Very Tedious One To Recognize The Similar User Documents From The Entire Social Media Text Message. Furthermore, Online Comments Are Characteristically Categorized By A Sparse Feature Space, Which Makes The Respective Emotion Classification Task A Complex One. Methodology: Three Major Contributions Were Done In This Work In Order To Rectify These Problems, They Are: Development Of A Novel Mutation Bat Optimization Based Sparse Encoding (MBO-SC) Which Transforming The Sparse Low-Level Features Into Dense High-Level Features, Was The 1st Contribution, Next Is, An Enhanced Weight Based Convolutional Neural Network (EWCNN) To Target-Specific Layer. It Influences The Semantically EWCNN Classifier To Include Semantic Domain Knowledge Into The Neural Network To Bootstrap Its Inference Power And Interpretability. Fuzzy Clustering Algorithm Is Proposed To Minimize The Similarity Among Two Documents. Uses: It Is Quite Constructive In Recommending Products, Collecting Public Opinions, And Predicting Election Results. Proposed Work Is Distinguished With The Existing Methods, With The Metrics Such As: Precision, Recall, Sensitivity, Specificity, F-Measure And Accuracy. From The Experimental Result It Is Confirmed That The Quality Of Learned Semantic Vectors And The Performance Of Social Emotion Classification Can Be Enhanced By Proposed Models.
Blinkered

In an effort to keep up with a world of too much, life hackers sometimes risk going too far.The open access edition of this book was made possible by generous funding from the MIT Libraries. Life hackers track and analyze the food they eat, the hours they sleep, the money they spend, and how they're feeling on any given day. They share tips on the most efficient ways to tie shoelaces and load the dishwasher; they employ a tomato-shaped kitchen timer as a time-management tool.They see everything as a system composed of parts that can be decomposed and recomposed, with algorithmic rules that can be understood, optimized, and subverted. In Hacking Life, Joseph Reagle examines these attempts to systematize living and finds that they are the latest in a long series of self-improvement methods. Life hacking, he writes, is self-help for the digital age's creative class.Reagle chronicles the history of life hacking, from Benjamin Franklin's Poor Richard's Almanack through Stephen Covey's 7 Habits of Highly Effective People and Timothy Ferriss's The 4-Hour Workweek. He describes personal outsourcing, polyphasic sleep, the quantified self movement, and hacks for pickup artists. Life hacks can be useful, useless, and sometimes harmful (for example, if you treat others as cogs in your machine). Life hacks have strengths and weaknesses, which are sometimes like two sides of a coin: being efficient is not the same thing as being effective; being precious about minimalism does not mean you are living life unfettered; and compulsively checking your vital signs is its own sort of illness. With Hacking Life, Reagle sheds light on a question even non-hackers ponder: what does it mean to live a good life in the new millennium?
AMD RYZEN

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Designing the architecture of a blockchain platform: the case of Alastria, a national public permissioned blockchain

The financial services industry is experiencing - or better said, is suffering - an enormous paradigm shift. New technologies, combined with new consumers, are redefining the way financial institutions design products and deliver services. "Visa, for example, is working on bitcoin remittance services, while Mastercard and AMEX are investing in ABRA and the Digital Currency Group, two blockchain startups" (Pejic, 2019). Furthermore, Mastercard has filed four blockchain patents to improve settlement systems. The Alastria case is a still rare phenomenon of national blockchain as a country territorial and jurisdictional case of public permissioned blockchain oriented to combine the satisfaction of public and private interests with full legal compliance.
Reliable Image Capturing for Industry and Consumers

Abstract Taking sharp pictures despite fast changing lighting and ambient conditions, taking snapshots without blurring, recognizing traffic signs or road markings in driver assistance systems, or identifying dangerous situations in surveillance systems – all of this is possible today with the help of modern cameras. But how are the different criteria for still and video images of cameras reliably measured and evaluated? And how do they compare with models from other suppliers? When testing image stabilization technology, for example, hexapods are used to simulate the movement that cameras are subjected to while taking pictures.
Survival Analysis and the EM Algorithm

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. 'Data science' and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov Chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. Each chapter ends with class-tested exercises, and the book concludes with speculation on the future direction of statistics and data science.
Study shows nonprofits struggling with impact measurement

A new study conducted by Oracle NetSuite, a provider of cloud‐based software for charities, shows that nonprofits continue to struggle with showing the outcome of their work. The study, Connecting Dollars to Outcomes , found that just 29% of nonprofits are able to effectively measure the impact of their dollars invested in programming, despite the widespread belief among nonprofit leaders that doing so is fundamental to their success.
An Image Compression Based Technique to Watermark a Neural Network

While neural networks have made considerable progress in the area of digital representation, training of neural models requires an enormous data and time. It is well known that the use of trained models as initial weights often leads in less training error than un-pre-trained neural networks. We propose in this paper a digital watermarking system for neural networks. We formulate a new challenge: the integration of watermarks into neural networks through discrete cosine transform (DCT) based approach. For discrete wavelet transform (DWT)-based digital image watermarking algorithms, additional performance enhancements could be obtained by combining DWT with DCT. Throughout the neural networks, we also describe specifications, embedded conditions, and attack forms of watermarking. The technique presented here does not affect the network performance in which a watermark is positioned as the watermark is embedded while the host network is being trained. Finally, we perform detailed image data experiments to demonstrate the potential of neural networks watermarking as the basis for this research attempt.
GMM-UBM Based Modeling for Language Identification using New Feature Vectors

The most of the existing LID systems based on the Gaussian Mixture model. The main requirement of the GMM based LID system is it require large amount of speech data to train the GMM model. Most of the Indian languages have the similarity because they are derived from Devanagari. Even though common phonemes exists in phoneme sets across the Indian languages, each language contain its unique phonotactic constraints imposed by the language. Any modeling technique capable of capturing all these slight variations imposed by the language is one of the important language identification cue. To model the GMM based LID system which captures above variations it require large number of mixture components.To model the large number of mixture components using Gaussian Mixture Model (GMM), the technique requires a large number of training data for each language class, which is very difficult to get for Indian languages. The main objective of GMM-UBM based LID system is it require less amount of training data to train(model) the system. In this paper, the importance of GMM-UBM modeling for language identification (LID) task for Indian languages are explored using new set of feature vectors. In GMM-UBM LID system based on the new feature vectors, the phonotactic variations imparted by different Indian languages are modeled using Gaussian Mixture model and Universal Background Model (GMM-UBM) technique. In this type of modeling, some amount of data from each class of language is pooled to create a universal background model. From this UBM model each model class is adapted. In this study, it is found that the performance of new feature vectors GMM-UBM based LID system is superior when compared to conventional new feature vectors based GMM LID system.
An Enhanced Generalized Sequential Pattern Classification for Sequence Datasets

A Niche approach for classifying sequence Datasets is achieved by the use of EGSP (Enhanced Generalized sequential pattern) algorithm. The EGSP brings out a prediction model for working with the Sequence Datasets in the domain of Gene and Protein datasets. The Method proceeds by the way of generalizing the datasets of both the supervised and Semi-supervised data. The generalization brings out the candidate sequences which paves path for a distinct component extraction. The sequences are generated based on the threshold value which is then followed by applying the EGSP algorithm which brings out the sequential pattern from the pruned sequences. The extracted sequential pattern is then clustered using a gene clustering algorithm. The algorithm (MNBC) Modified Naïve Bayes Classification computes the probabilistic components for each class. The accuracy obtained is far better than the traditional classification algorithms. The resultant classification provides a solution for prediction methods for the selected domain and its applications. The algorithm used gives an upper hand over the computational costs which have been drastically minimized over the existing methods.
Boolean Logic

The article describes and explains Boolean logic (or Boolean algebra) in its two principal forms: that of truth-values and the Boolean connectives and, or, and not, and that of set membership and the set operations of intersection, union and complement. The main application areas of Boolean logic to know­ledge organization, namely post-coordinate indexing and search, are introduced and discussed. Some wider application areas are briefly mentioned, such as: propositional logic, the Shannon-style approach to electrical switching and logic gates, computer programming languages, probability theory, and database queries. An analysis is offered of shortcomings that Boolean logic has in terms of potential uses in know­ledge organization.
Building Models

In this chapter, we use first-order logic to build by formal definitions higher-level concepts from more fundamental ones; in particular, we present the types and operations that are essential for the construction of mathematical models. We start with a general explanation of how we can introduce mathematical theories from scratch by characterizing their fundamental entities via logical axioms; on top of these we can later introduce other entities by explicit definitions. Then we present the theory of sets as the prime example of an axiomatic theory; based on this theory we introduce a collection of ‘types’ and ‘type constructors’ that also encompass in a certain sense functions and predicates as objects of the theory.
PENENTUAN DAERAH PRIORITAS PELAYANAN AKTA KELAHIRAN DENGAN METODE K-NN DAN K-MEANS

Population Data is individual data or aggregate data that is structured as a result of Population Registration and Civil Registration activities. Birth Certificate is a Civil Registration Deed as a result of recording the birth event of a baby whose birth is reported to be registered on the Family Card and given a Population Identification Number (NIK) as a basis for obtaining other community services. From the total number of integrated birth certificate reporting for the 2018 Population Administration Information System (SIAK) totaling 570,637 there were 503,946 reported late and only 66,691 were reported publicly. Clustering is a method used to classify data that is similar to others in one group or similar data to other groups. K-Nearest Neighbor is a method for classifying objects based on learning data that is the closest distance to the test data. k-means is a method used to divide a number of objects into groups based on existing categories by looking at the midpoint. In data mining preprocesses, data is cleaned by filling in the blank data with the most dominating data, and selecting attributes using the information gain method. Based on the k-nearest neighbor method to predict delays in reporting and the k-means method to classify priority areas of service with 10,000 birth certificate data on birth certificates in 2019 that have good enough performance to produce predictions with an accuracy of 74.00% and with K = 2 on k-means produces a index davies bouldin of 1,179.
Evaluation of Differential Evolution Algorithm with Various Mutation Strategies for Clustering Problems

Evolutionary Algorithms (EAs) based pattern recognition has emerged as an alternative solution to data analysis problems to enhance the efficiency and accuracy of mining processes. Differential Evolution (DE) is one rival and powerful instance of EAs, and DE has been successfully used for cluster analysis in recent years. Mutation strategy, one of the main processes of DE, uses scaled differences of individuals that are chosen randomly from the population to generate a mutant (trial) vector. The achievement of the DE algorithm for solving optimization problems highly relies on an adopted mutation strategy. In this paper, an empirical study was presented to investigate the effectiveness of six frequently used mutation strategies for solving clustering problems. The experimental tests were conducted on the most widely used data set for EAs based clustering, and the quality of cluster solutions and convergence characteristics of DE variants were evaluated. The obtained results pointed out that the mutation strategies that use the guidance information from the best solution mange to find more stable results whereas the random mutation strategies are able to find high quality solutions with slower convergence rate. This study aims to provide some information and insights to develop better DE mutation schemes for clustering.
SPY ROBOT FOR SURVEILLANCE USING RASPBERRY PI CONTROLLER .

Nowadays robot plays a vital role in our day to day life, thus reducing human activities. The main function of Spy robot is to roam around high sensitive region and provide video information from the required environment to the remote monitoring station. In this project, one can control the robot using Bluetooth reducing human effort. The user can continuously monitor the surrounding activities of robot location with live video streaming using wireless camera from the robot. This robot is modeled with Raspberry Pi microcontroller which control the robot behavior. The proposed spy robot is used to find smugglers infiltration.
Recommender System using Content Based Filtering for News Portal in Indonesia

Nowadays there is much news on the internet. It makes the reader become information overload. The reader does not know the most important news for them. The digital era, especially in Indonesia, generated data in Bahasa very fast that referred to as big data. Data mining by process big data can collect the data insight that the reader already read. This paper proposes a new model to proceed with Bahasa news and use the TF-IDF method to collect the feature of the article. Cosine similarity from the news article used to rank the new unknown articles to recommend articles based on their preference. we can filtering the stream of information and highlight the most likely article they will read but based on their preference that we already collect implicitly from the article that they read it, it’s a scroll depth of the article they read.Then we can serve the news more personalized from what they love to read.
Second-Order Optimization Techniques

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.