Reviewerly/exHarmony huggingface dataset:
Robust Multi-Output Learning with Highly Incomplete Data via Restricted Boltzmann Machines

In a standard multi-output classification scenario, both features and labels of training data are partially observed. This challenging issue is widely witnessed due to sensor or database failures, crowd-sourcing and noisy communication channels in industrial data analytic services. Classic methods for handling multi-output classification with incomplete supervision information usually decompose the problem into an imputation stage that reconstructs the missing training information, and a learning stage that builds a classifier based on the imputed training set. These methods fail to fully leverage the dependencies between features and labels. In order to take full advantage of these dependencies we consider a purely probabilistic setting in which the features imputation and multi-label classification problems are jointly solved. Indeed, we show that a simple Restricted Boltzmann Machine can be trained with an adapted algorithm based on mean-field equations to efficiently solve problems of inductive and transductive learning in which both features and labels are missing at random. The effectiveness of the approach is demonstrated empirically on various datasets, with particular focus on a real-world Internet-of-Things security dataset.
Research on Development and Application for Micro-Course Resources of Advanced Mathematics Under Background of Educational Informatization

As a public compulsory course offered for the most of majors, Advanced Mathematics will significantly influence learning of subsequent courses during university and play an important role in cultivating logical thinking of students. However, due to massive contents and short class hour, it is impossible for teachers to spend too much time in imparting knowledge beyond teaching requirements. In view of this, this research will solve existing teaching and learning problems through deeply discussing topics about micro-course teaching, completely searching micro-course related resources, scientifically designing special micro-course, specially recording micro-course teaching and strengthening application of micro-course teaching resources in the course of research and development for micro-course teaching resources of Advanced Mathematics .
Review of Existing Consensus Algorithms Blockchain

In recent years, blockchain technology has begun to attract close attention. The blockchain solved the problem of replacing the original centralized ledger with a low level of trust, on a decentralized form with a high level of trust, which has various objects, or, in other words, checking nodes. The main feature of the blockchain technology is a consistent algorithm that decides how an agreement is reached on adding a new block between all nodes in the checking network. Blockchain algorithms can be divided into two main groups. There is consensus based on evidence. This group requires that nodes connecting to the inspection network show that they are more qualified than others to do the extra work. The second type is consensus, which is based on voting and requires that the nodes in the network exchange the results of the verification of a new block or transaction before making a final decision. This article provides an overview of existing blockchain consensus algorithms.
Video Profile Sebagai Media Penunjang Promosi Pada Sdk Penabur Gading Serpong

The tight competition between schools in the era of globalization today, requires that the school Ivory Serpong Sower SDK to do the marketing strategy as attractive as possible, in order to enhance the new prospective learner interests as well as improve the quality of and professionalism in the management of the school. It is a promotional effort is used to market products or services to the public, so that they are more familiar with or know the product or service being marketed. Current media information and promotions that use the SDK Sower Ivory Serpong still be printed in the form of banners, brochures and more, so there are still many who have not yet learned about SDK Sower Gading Serpong. The purpose of this research is to inform and promote school SDK Sower Ivory Serpong. Based on a needs analysis is required of media promotion in the form of a video profile, to support information and promotion more attractive and effective by using the software Adobe Premier and Adobe Photoshop. Research methods used namely collecting data through observation, interviews and literature studies, Media Production and concept i.e. preproduction, production and postproduction. Through the design of Video media Profile of this SDK Sower Ivory Serpong can better known to many people and attracting parents to enroll his son in schools � SDK Sower Ivory Serpong
Verifying the Integrity of Private Transaction Information in Smart Contract using Homomorphic Encryption

Blockchain-based Smart Contract allows reliable enforcement of the programmatically specified agreement between distributed agents. However, during the process of verifying the result of transaction execution with distributed consensus protocol, private information such as fund amount being transferred can be exposed to others. To hide such sensitive information while keeping the integrity of the Smart Contract instances, we employ a homomorphic encryption technique. With this encryption technique, the validity of the arithmetic operation in a transaction can be checked without decrypting the operands. For instance, a remaining balance after fund transfer can be validated while the exact amount of the transferred fund and the final balance are kept in a ciphertext. In this work, we discuss the novel system architecture for verifying Smart Contracts instances with homomorphic encryption and evaluate its efficiency.
Design of Point of Sales (POS) Information Systems Based on Web and Quick Response (QR) Code

Sales is the activity of trade in goods and services to customer.Process sales and service transactions should be properly Tawakkal Shoes Store is a business engaged in the sale of shoes and sandals that requires a system that can simplify the process of data processing sales transactions and making it more effective and efficient.This research will be designed a web-based system and QR Code that is used to process sales transactions, the recording of incoming goods, and report the results of operations.The method used in this research is the method of observation, interviews, documentation and software simulation.The design of the system done by using a MySQL database and software XAMPP.The process of creating a software program code using Visual Code, and the programming language used are PHP and HTML.From this research will produce a webbased system and QR Code that used to process sales transactions and processing data easier, safer, effective, and efficient.
Internet of Things Devices: Digital Forensic Process and Data Reduction

The rapid increase in the pervasiveness of digital devices, combined with their heterogeneous nature, has culminated in increasing volumes of diverse data, a.k.a. big data that can become subject to criminal or civil investigations. This growth in big digital forensic data (DFD) has forced digital forensic practitioners (DFPs) to consider seizing a wider range of devices and acquiring larger volumes of data that can be pertinent to the case being investigated. This, in turn, has created an immense backlog of cases for law enforcement agencies worldwide. The method of data reduction by targeted imaging, combined with a robust process model, however, can assist with speeding up the processes of data acquisition and data analysis in IoT device forensic investigations. To this end, we propose an IoT forensic investigation process model, IoT-FIPM, that can facilitate not only the reduction of the evidentiary IoT data, but also a timely acquisition and analysis of this data.
Teacher’s Strategy in Solving EFL Students’ Problems in Learning English

Learning new language is never been so easy to master where there must be challenges that teachers face in classroom. The aim of this research is to investigate an English teacher in teaching English as a foreign language to junior high school students at Ma’had Tahfidzul Qur’an, Samarinda, East Kalimantan, Indonesia. The matters to be investigated include barriers the teacher faced as well as her strategies to solve the problems. The result of semi-structured interview showed that the biggest challenge faced by the English teacher is the condition where the students are less-motivated in learning English. Several strategies are used by the teacher such as encourage the students through verbal motivation as well as provide joyful activities such as conducted different kind of games to build positive mood and interest of students in leaning a new language. The use of proper teaching strategies is demanded in order to have English learning become students’ habit. Therefore, implication of language teaching methodologies is also discussed in this research.
Predicting Potential Electronic Serials Use

When assessing electronic serials, librarians often review usage reports for subscribed serials but know relatively little about the potential use of unsubscribed titles. This paper discusses methods for predicting future use, including examining use of similar materials, citation metrics, interlibrary loan requests, denial/turnaway reports, and failed link resolver requests. Each indicator can tell librarians something different about how users are discovering and attempting to access materials. This paper also shares preliminary data from two case studies that detail an attempt to correlate prior year indicators of demand with first year use data of new electronic serials acquisitions.
Vehicular Volunteer Computing (VVC): A Novel Paradigm for the Volunteering of Vehicular Resources; Opportunities and Challenges

Modern applications and scientific research involve resource demanding tasks such as storage, retrieval and analysis of large and complex data. To meet the huge resource requirement, vehicular volunteer computing is proposed in this paper. With the advancements in vehicular communication technologies and embedment of computing resources in vehicles, a large number of powerful computing resources would be available on roads and at parking lots which can be employed to make the execution of projects possible without relying on dedicated servers and supercomputers. In our proposed vehicular volunteer computing, compute intensive task hosted on a server is divided into parts and scheduled to volunteered vehicles for execution. This study examines the characteristics as well as opportunities presented by vehicular volunteer computing for the accomplishment of important projects such as scientific research and other computation intensive applications. Research challenges that need to be overcome for vehicular volunteering computing to be more efficient are also presented in this study.
Accelerated Generation of Spatiotemporal Atlas through Fast Template Matching

A spatiotemporal atlas refers to a standard image sequence that represents the general motion pattern of the targeted anatomy across a group of subjects.Recent years have witnessed an increasing interest in using spatiotemporal atlas for scientific research and clinical applications in image processing, data analysis and medical imaging.However, the generation of spatiotemporal atlas is often time-consuming and computationally expensive due to the nonlinear image registration procedures involved.This research targets at accelerating the generation of spatiotemporal atlas by formulating the atlas generation procedure as a multi-level modulation (M-ary) classification problem.In particular, we have implemented a fast template matching method based on singular value decomposition, and applied it to generate high quality spatiotemporal atlas with reasonable time and computational complexity.The performance has been systematically evaluated on public accessible data sets.The results and conclusions hold promise for further developing advanced algorithms for accelerating generation of spatiotemporal atlas.
Clustering of Javanese News in Krama Alus Level with Javanese Stemming

News is generally published by news providers. News providers in publishing news are sometimes not grouped into several news categories. This allows readers difficulty in finding news because the news is not grouped. This study group Krama Alus Javanese news for readers can search for news according to categories with the Hierarchical and K-Means Algorithms. Research data uses news documents from the JOGJATV online news portal. In this study, the data is processed through text preprocessing. Text preprocessing consists of five processes, one of which is stemming. Stemming is used to adapt the Nazief and Adriani algorithms which are adjusted to the rules in Javanese. The method used to process the results of text preprocessing is hierarchical clustering combined with k-means. Hierarchical clustering is used to determine the number of clusters and centroids of each cluster. The results showed that the stemming process required the addition of basic words and stemming rules. While for the clustering results there are 18 clusters. The evaluation of the overall cluster structure with Average Silhouette Width (ASW) shows a value of 0.9142. This value indicates that a cluster has different characteristics from other clusters so that the news documents are in the right group. The clustering results are also validated by Experts with good results, namely 11 clusters that can be labeled while 7 clusters with labels are not specific.
Quality Evaluation of Marine Statistical Data on the Basis of Clustering Algorithms

Cui, Y., 2019. Quality evaluation of marine statistical data on the basis of clustering algorithms. In: Li, L.; Wan, X.; and Huang, X. (eds.), Recent Developments in Practices and Research on Coastal Regions: Transportation, Environment and Economy. Journal of Coastal Research, Special Issue No. 98, pp. 151–154. Coconut Creek (Florida), ISSN 0749-0208.The quality of marine statistical data is the life of marine statistical work, and it is the key to reflecting the level of marine statistical work, which is related to the credibility of marine statistical departments. Therefore, in marine statistics work, we need to establish perfect marine statistical data quality evaluation criteria. Only by meeting the basic standards will we gradually establish a comprehensive and systematic marine statistical data quality monitoring system, which will guarantee the authority of the marine statistical data. This paper further improves the quality of marine statistical data in China by using a clustering algorithm.
Credit Card Risk Detection based on Feature-Filter and Fraud Identification

Credit card fraud can destabilise economies, reduce confidence between customers and banks and affect other individuals or companies negatively. The primordial objective of banks is to identify fraudulent transactions with a high level of accuracy to reduce the training time and the costs of the manual investigation activity. This paper proposes a credit card fraud detection method using Random Forest as dimensionality reduction algorithm and Isolation Forest as a fraud detection algorithm. The method is applied to a large dataset in purpose to investigate their fraud detection accuracy. The experimental results of this study confirms the advantages and effectiveness of the proposed method in different criteria: accuracy, sensitivity and training time.
Applying CLP to machine translation: A Greek case study

In this research we concentrate on how CSP techniques can contribute to computational linguistics.For this purpose, we tested CP techniques on a linguistic problem of polysemy in machine translation.Ambiguity arises almost every time computers try to deal with human language.In information retrieval, the computer often provides information about alternative meanings of the search terms, meanings that we have no interest in.In machine translation, different meanings of an English word may be expressed by very different words in the target language, so it is important to determine which meaning of the English word the author had in mind and this is where a computer fails.Over and over, attempts to use computers to process human language have been frustrated by the computer's limited ability to deal with polysemy (Miller 2001).A toy system has been designed for translating between English and Greek.It is based on 6 sentences that, using the same lexicon, can be expanded to slightly more phrases.The translation is based on the results of two parsers -the English and the Greek one.Parsing uses a set of constraints (both semantic and grammatical).The main objective of this research is to evaluate Backtracking as one of CSP algorithms for machine translation.We used PROLOG to write an application because it is the only programming language that supports backtracking and where the choice made by the program can be traced back at every stage.
Technical Methodology for Identification of Criminals

Background/Objectives: This paper plans to locate the effective strategy to characterize the first implications that police photos as a methods for identification of culprits had. Methods/Statistical analysis: To study this goal, it doesn't matter the social method, notwithstanding the way that this method made it feasible for unexploited ranges to be incorporated interestingly into the historical backdrop of photography. The social strategy did not adequately investigate every one of the levels of importance of these photos. Rather, this paper is fond of saying a little in regards to what we call the specialized technique approach or physical approach. Findings: To locate the productive approach, this review depended on the specialized strategy, that is, it continued scrutinizing the very rule of photography. As such, the two basic physical characteristics of photos, which consist of the optical attributes of the lens and the chemical properties of the photosensitive surface. The policeman uses such elements of photography, without turning to hypothetical information of the properties of photography. It is our part to clarify how such uses are made conceivable by the technical standards of photography. By technical approach, it doesn't just mean scrutinizing the way photography functions, but it likewise implies an investigation into the way the human visual discerning framework works. Asking into the implication of legal photography implies endeavoring to comprehend the first importance the innovator of police photography and its consequent clients gave it. At that point, it implies asking all the more profoundly into the mechanical, and physical standards of photography that make conceivable the numerous capacities utilized by the police, and also asking into the way our visual insightful framework works. Improvements/Applications: It is just a felt that begins from an investigation into the original circumstance of photos that will keep us from making improper utilizations of our current knowledge.
SACO: Past, Present, and Future

This article discusses the history and development of the SACO (Subject Authority Cooperative) Program of the Program for Cooperative Cataloging (PCC). The current proposal process is examined. The University of California, Davis Library is presented as a participating institution, with some forward-looking utilization of Library of Congress Subject Headings (LCSH).
Integrating Relation Constraints with Neural Relation Extractors

Recent years have seen rapid progress in identifying predefined relationship between entity pairs using neural networks (NNs). However, such models often make predictions for each entity pair individually, thus often fail to solve the inconsistency among different predictions, which can be characterized by discrete relation constraints. These constraints are often defined over combinations of entity-relation-entity triples, since there often lack of explicitly well-defined type and cardinality requirements for the relations. In this paper, we propose a unified framework to integrate relation constraints with NNs by introducing a new loss term, Constraint Loss. Particularly, we develop two efficient methods to capture how well the local predictions from multiple instance pairs satisfy the relation constraints. Experiments on both English and Chinese datasets show that our approach can help NNs learn from discrete relation constraints to reduce inconsistency among local predictions, and outperform popular neural relation extraction (NRE) models even enhanced with extra post-processing. Our source code and datasets will be released at https://github.com/PKUYeYuan/Constraint-Loss-AAAI-2020.
The Challenges of Azerbaijani Transliteration on the Multilingual Internet

Several scripts have been adopted so far in Azerbaijan in different periods. Literary manuscripts and political documents developed in each of the adopted scripts are of great importance and have to be transliterated for the next generation. However, adoption of different scripts and their frequent changes lead to the emergence and dissemination of various transliteration versions on the web. This article touches upon the challenges of Azerbaijani-English transliteration process in real life and online. In this regard, the significance and dominating status of the English language on the Internet and throughout the globe is explored. Adoption of a unique transliteration standard for Azerbaijani language may contribute to the solution to this problem. The standards for the transliteration of the Azerbaijani language with others are analyzed, and the inevitability of a new approach to Azerbaijani-English transliteration is emphasized. Moreover, the article underlines future contribution of the proposed transliteration system for machine translation system of Azerbaijani-English language pair.
Model-based Collaborative Filtering with Transparency using Linear Regression

A collaborative filtering technique with transparency using linear regression is proposed. In this study, the transparency is assumed as a role which users can know how the score is calculated. The proposed linear regression model presents regression coefficients to users. This study evaluates application of regularization and dimensionality reduction to estimate regression coefficient, and performs a score prediction experiment which analyze prediction accuracy, computing time, and obtained regression coefficients using five benchmark datasets for collaborative filtering. According to the experimental results, the proposed linear regression model with L2 regularization achieves the best prediction accuracy in the nine estimation methods considered for the regression coefficients. In addition, its prediction accuracy is similar level to Factorization Machines. The learning time of the proposed linear regression model is 24.9 to 1584 times faster than Factorization Machines. In the analysis of regression coefficients, variety in regression coefficient values are found in without regression and with L2 regression cases, and this suggests possibility of which the learned models are personalized.
Multi-Level Image Thresholding via Nonlinear Fitting of the Histogram

This paper proposes a novel scheme for multi-level image thresholding.The 1D cumulative histogram is curve-fitted to a polynomial of nonlinear basis function, the hyperbolic tangent (tanh).The thresholds are the positions of the minima of the first (odd) derivative of the fitting polynomial, since the histogram is the derivative of the cumulative histogram.The tanh function is considered as an approximation to the integral of the Gaussian function.However, better results were obtained by fitting the derivative of the polynomial to the histogram.The scheme is a direct solution (solving a linear system) and does not require iterations or exhaustive search.Some results are presented to demonstrate the effectiveness of the proposed scheme.