Reviewerly/exHarmony huggingface dataset:
Performance Improvement of Open Source Based Business Intelligence System Using Database Modeling and Outlier Detection

With all the advanced technology nowadays, new data is being generated every minute. For example, the average size of the computer’s hard disk is 10 gigabytes in 2000, today on the Facebook website has increased 500 terabytes of new data per day [1]. Data is growing rapidly, but it is not enough valuable. Thus, it is important to extract information that is useful in the future from a large amount of data. Business intelligence (BI) systems make a prediction that supports a business decision by analyzing collected data [2]. However, the accuracy of prediction depends on a data quality. In practice, data is usually a very low quality that includes many incomplete and anomaly data. Moreover, another problem is if data size increases, query response will be slow. Previous research work, we proposed a framework based on open-source technologies for the BI systems that possibility to analyze big data efficiently and apply it to the supermarket’s BI system. Under this solution, we have studied Hadoop data storage system, Hive data warehouse software, Sqoop data transmission tool and etc., successfully implemented them. In this paper, we have added anomaly detection stage on the proposed framework to improve information about related products that are purchased together by eliminating anomaly. Also, we have made an experimental study to improve the speed of time-dependent reports by applying the dimensional model to Hive data warehouse. In dimensional model data is stored in context of the single table (centralized context), and in relational model the context is distributed over many tables. As a result of the experimental study, the dimensional model is more efficient; its query response time is shown to be at least two times faster than the relational model based data warehouse.
A Hybrid Recommendation Algorithm to Address the Cold Start Problem

In this age where there are a lot of available data, recommender systems are a way to filter the useful information. A recommender system's purpose is to recommend relevant items to users, and to do that, it requires information on both, data from users and from items, to better organize and categorize both of them. There are several types of recommenders each best suited for a specific purpose, and with specific weaknesses. Then there are hybrid recommenders, made by combining one or more types of recommenders in a way that each type suppresses or at least limits the weaknesses of the other types. A very important weakness of recommender systems occurs when the system doesn't have enough information about something and so, it cannot make a recommendation. This problem known as a Cold Start problem is addressed in this study. There are two types of Cold Start problems: those where the lack of information comes from a user (User Cold Start) and those where it comes from an item (Item Cold Start). This article's main focus is on User Cold Start problems. A novel approach is introduced that combines clients' segmentation with association rules. Although the proposed solution's average precision is similar to other main Cold Start algorithms it is a simpler approach to most of them.
Estimate of Discharge of Lithium-Ion Batteries

The present work has the objective to estimate the curves of discharge of Lithium-Ion batteries. Several methods were tested for comparative analysis. The used database set of batteries were made available by NASA. All of the estimates had a good dash beyond the expected estimate. We used a Artificial Neural Network, Least Squares, Kalman Filter with least squares, ARX e ARMAX models. The evaluations of each method were done according to the computational cost and the coefficient of determination.
Using a permutation representation genetic algorithm to implement a complex pick up/drop off mathematical model

The Variable Message Sign Problem (VMSP) aims to optimise the delivery, collection and maintenance of Variable Message Signs. Firstly, the problem was formulated as a rigorous mathematical model. It was then reformulated using a permutation representation removing the need for some hard constraints and the formation of illegal sub-tours within the solution. The reformulated model was shown to be solvable using different artificial intelligence search techniques. The problem of parameter selection was then solved using self-adaption, which produced superior solutions with >99.9% confidence by avoiding numerous local optima on the fitness landscape.
Analysis of neural networks efficiency for determining positions of corrupted bytes

A lot of files and data, in general, are transferred throughout the networks. But the data may be corrupted by intrusions or package loss so, the executable files may be marked as nonexecutable and violate the local network policy. Thus, it's necessary to detect such files. In this paper, we present a novel method for detecting broken bytes of a file, so the corrupted files may be detected. Also, the positions of wrong bytes might be helpful in restoring the original file content.
Medical Diagnosis Based on Nonlinear Manifold Discriminative Projection

In recent years, medical diagnosis based on machine learning has become popular in the interdiscipline research of computer science and medical science. It is closely related with classification, which is one of the important problems in machine learning. However, the traditional classification algorithms can hardly appropriately solve high-dimensional medical datasets. Manifold learning as nonlinear dimensionality reduction algorithm can efficiently process high dimensional medical datasets. In this paper, we propose an algorithm based on Nonlinear Manifold Discriminative Projection (NMDP). Our algorithm incorporates the label information of medical data into the unsupervised LLE method, so that the transformed manifold becomes more discriminative. Then we apply the discriminant mapping to the unlabeled test data for classification. Experimental results show that our method exhibits promising classification performance on different medical data sets.
What Is Semantically Important to “Donald Trump”?

In the recent years, there is a growing interest in combining explicitly defined formal semantics (in the forms of ontologies) with distributional semantics “learnt” from a vast amount of data. In this paper, we try to bridge the best of the two worlds by introducing a new metrics called the “Semantic Impact” together with a novel method to derive a numerical measurement that can summarise how strong an ontological entity/concept impinges on the domain of discourse. More specifically, by taking into consideration the semantic representation of a concept that appears in documents and its correlation with other concepts in the same document corpus, we measure the importance of a concept with respect to the knowledge domain at a semantic level. Here, the “semantic” importance of an ontology concept is two-fold. Firstly, the concept needs to be informative. Secondly, it should be well connected (strong correlation) with other concepts in the same domain. We evaluated the proposed method with 200 BBC News articles about Donald Trump (between February 2017 and September 2017). The preliminary result is promising: we demonstrated that semantic impact can be learnt: the top 3 most important concepts are Event, Date and Organisation and the least essential concepts are Substance, Duration and EventEducation. The crux of our future work is to extend the evaluation with larger datasets and more diverse domains.
Information and Communication Technology for Development (ICT4D)

"Information and Communication Technology for Development (ICT4D)." The Information Society, 35(2), pp. 107–108
The Analysis and Design of Ship Monitoring System Based on Hybrid Replication Technology

As the core of informatization, data has a huge significance to the development of information-based enterprises. Data replication technology is an important approach to solve the problem of enterprise data sharing based on distributed database system. It plays a crucial role in promoting business integration of enterprises and institutions, improving data quality, enhancing data sharing and improving the application level of back-end big data analysis [1]. It is necessary to do research for making a good data management of the distributed database application system, synchronizing the data to the data, preventing data conflicting and being able to synchronize or asynchronous replication. Combining with the database design model of the ship monitoring and control system, this paper mainly described how to complete the construction of distributed database system using Oracle, based on advanced replication technology named as the combination of multi-agent replication and materialized views hybrid replication technology.
Design and Implementation of Search-Box in Instant Messaging Application

Phenomenon of communication technology on smartphones can be used to meet the needs of actual real time communication. Communication consists of several factors such as text message, video, audio exchange or personal notes, personal documents and also file sharing in-between individual or in chat/group room. As we know most of the percent people used Instant Messaging application to communicate and there are several such application available. A search box is a technique used in this application to enhance the communication between users. The basic feature of search box is, it replies to the query on any word given by the end user. The search box is used to provide the meaning of the word, user enter in the search box. This is the main concept we find to work on in Instant Messaging Application.
A Systematic Framework for Malicious Traffic Detection Based on Feature Repository

Machine learning becomes an effective method to detect malicious traffic. With the proliferation of network traffic, malicious traffic categories are greatly increased, which puts forward higher requirements for the computation time and detection accuracy of machine learning. A feature selection framework is proposed to balance the computation time and detection accuracy. First, we construct a feature repository of traffic information with high dimensions. In order to reduce the computation time and minimize the loss of accuracy, we investigate the feature selection algorithms. The algorithm based on chi-square test and xgboost algorithm are adopted to evaluate the proposal. The experiments on CTU dataset show that the proposal can reduce the computation time while ensuring the accuracy.
Relationship Between Use of a Learning Support System for Intellectual Property Law Education and Knowledge Acquisition

We analyze the relationship between use of a learning support system for face-to-face lectures on intellectual property law and knowledge acquisition. We first describe a system that we are currently developing and operating. We next analyze the relationship between system use and knowledge acquisition over the past 5 years (FY 2013–2017) for engineering students participating in these lectures. The analysis results show that daytime students frequently used the system for mid-term and final exams, and that nighttime students tended to access the system throughout the term. Finally, we analyze relationships between login count, video system usage time, and exercise system usage times in FY 2017. The results suggest that the video system is useful for acquiring basic knowledge, and that the exercise system promotes acquisition of applied knowledge. The exercise system for studying legal texts, logical expressions, and logic circuits was well received by engineering students, suggesting that system usage facilitates understanding of legal texts. In future work, we will improve the exercise system for logical expressions and logic circuits.
SOME DILEMMAS REGARDING DIGITAL MEDIA (TECHNOLOGY) AND EDUCATION

This paper is focused on parents, educators, academics and all those concerned with education in Croatia, and explores how some teachers increasingly use digital media (technology) and interactive practices in order to direct their students’ curiosity and ability and help them in their civic engagement, collaboration with their peers all over the world, and to encourage them to direct their own learning. Media education, with critical thinking techniques, creative communication as well as computer, visual and acoustic literacy in its core, is the key of the 21st century approach to learning. Students of the 21st century point out the real examples of how digital media transform educational environments. Philosophers and other scientists have been discussing the nature of media and technology for a very long period of time. The diﬀerence is challenging because the two terms are used interchangeably in everyday language and that is the topic discussed in the paper.
Two-dimensional Evaluation of Educational Effect Using Out-of-class Studying Time and the Capacity for Homework Load as the Markers

The out-of-class studying time for students has recently become an important marker for the evaluation of educational effect. However, there is another argument that this alone is insufficient for the evaluation. In this study, the capacity for the homework load with students was added to the marker, and then, the educational effects of semester and quarter systems in a JABEE course were two-dimensionally evaluated. The evaluation was performed based on the questionnaire data from the first- to third- year JABEE-course students for required lectures, practice and experimental subjects. In the semester system, the two markers were stepwise increased as the year advanced. Additionally, the extension of the studying time with the same homework-load capacity was found in the quarter system.
Implementation of Land Merging Function Based on Big Data of ArcGIS Engine and C#.net

Based on the experience in the combination of theory and practice of ArcGIS Engine in the development of GIS technology in recent years, this paper combines with the map elements of important data sources of GIS basic frame to make the plot smaller than or equal to the given value merge into the largest surrounding land by C# language and COM components technology. And this research achievement plays an important and practical role in the land using and cadastre rectification.
Fault detection monitoring and controlling of induction motor using Zigbee

Our project is basically based on monitoring and controlling of the induction motor by the use of a Zigbee module. Microcontroller and other sensors play an important role in this monitoring and controlling of the induction motor. This type of monitoring helps to take preventive measures against induction motor when a fault occurs.
Processing and Communication Delays in EWS: On the Performance of the Earthcloud Prototype

A Seismic Alert System (SAS), also called Earthquake Warning System (EWS) or Earthquake Early Warning System (EEW or EEWS), represents one of the most important measures that can be taken to prevent and minimize earthquake damage. These systems are mainly used to detect P-waves and the faster seismic waves and to subsequently trigger an alarm about the incoming S-waves, the slower and most dangerous seismic waves. In some cases, distributed systems are also able to alert some locations before the impending P-waves strike them. This paper presents Earthcloud, a cloud-based SAS that aims to provide all the former capabilities while retaining financial accessibility. Earthcloud first results, generated from four months of data acquisition, are compared with those coming from other systems. In particular, the paper focuses on processing and communication delays, showing how the Earthcloud new detection strategy may minimize delays. Although a thorough test campaign with more sensor nodes is needed to assess performance reliably, especially for highly dense urban scenarios, initial results are promising, with total latencies for Earthcloud always kept under the 1-second mark, despite being at the expense of solid magnitude estimation.
Identity Concealment When Uploading Pictures of Patients in a Tele-Medicine System

Tele-medicine systems run the risk of unauthorized access to medical records, and there is greater possibility for the unlawful sharing of sensitive patient information, including children, and possibly showing their private parts. Aside from violating their right to privacy, such practices discourage patients from subjecting themselves to tele-medicine. The authors thus present an automatic identity concealment system for pictures, the way it is designed in the GetBetter tele-medicine system developed under a WHO/TDR grant. Based on open-source face- and eye-detection algorithms, identity concealment is executed by blurring the eye region of a detected face using pixel shuffling. This method is shown to be not only effective in concealing the identity of the patient, but also in preserving the exact distribution of pixel values in the image. This is useful when subsequent image processing techniques are employed, such as when identifying the type of lesions based on images of the skin.
An Analysis of Cryptographic Algorithms in IoT

The underlying vision of the internet of things (IoT) is to create a world where the real and the virtual realms are converging to create smart environments that makes energy, transport, cities, and many other areas more intelligent. With the IoT, the physical world is being interfaced through the things to the virtual world in heterogeneous environment. In heterogeneous environment, privacy and security are the major challenges. The secure information exchange is most critical pitfall to ensure the system security. This chapter gives a detailed analysis of cryptographic algorithms in IoT. A comparison of lightweight cryptography algorithms on basis of block size, key size, gate equivalents, and throughput is given. Moreover, the various security issues in IoT are discussed along with possible solution.
Network review emotion mining based on Domain Ontology

In the era of e-commerce, network reviews have become one of the most important means for people to exchange and obtain product information. It is an urgent need for consumers and enterprises to quickly and effectively obtain consumers' emotional attitude towards products from a large-scale network review. In order to better extract the product attributes involved in the network reviews and the emotional attitude of the consumer to the product attributes, this paper proposes a method of online comment sentiment analysis based on domain ontology. This method constructs the domain ontology, uses this ontology to extract product attributes, and constructs a domain sentiment dictionary to realize the emotional calculation of product attributes.
Research on Multi-module Feature Fusion of Putonghua Based on Genetic Algorithm

In order to improve the accuracy of pronunciation and evaluation of Chinese Putonghua, it is necessary to deal with the multi-module feature fusion of Putonghua. A comparative method of pattern recognition is proposed based on genetic algorithm (GA) and high order spectrum feature fusion of Putonghua pronunciation in Chinese Putonghua pronunciation standardization. The acquisition model of Chinese Putonghua speech pronunciation signal is constructed, and the filter processing and adaptive feature matching of the collected Mandarin speech signal are carried out to extract the statistical feature quantity of Putonghua speech signal. The method of spectral feature analysis was used to fuse the pronunciation features of Putonghua, and the correlation was detected according to the action attributes of phonetic organs. The high order spectrum characteristics of Chinese Putonghua speech pronunciation signal are extracted, and the modeling and adaptive learning of Chinese Putonghua speech pronunciation feature are carried out by means of statistical average detection and genetic evolutionary algorithm, and the adaptive optimization is carried out according to the genetic evolution process. The multi-module feature fusion of Putonghua is realized, which provides a standardized contrast mode for pronunciation. The simulation results show that the method has high accuracy in speech pronunciation feature detection and pattern recognition, improves the ability of multi-module feature fusion of Putonghua, and is effective and reliable for acoustic modeling of Putonghua pronunciation features. It has good application value in guiding the study and training of Putonghua.
A Probabilistic Matrix Factorization Recommendation Method Based on Deep Learning

In order to improve the accuracy of recommendation, a probabilistic matrix factorization recommendation method based on deep learning(PMFDL) is proposed. The method considers the influence of context information on the implicit feature of items and the influence of time factor on the implicit feature of users. In this paper, a convolutional neural network with attention mechanism is introduced to learn the implicit feature of items, and a long-term and short-term memory network is introduced to learn the implicit feature of users. Finally, we combine probabilistic matrix factorization(PMF) to predict recommendation results. After experimental verification, the experimental results show that the proposed PMFDL method is superior to Probabilistic Matrix Factorization(PMF) and Convolutional Matrix Factorization(ConvMF) in recommendation accuracy.
Salient object detection of fusing foreground seeds and center priori

In this paper, a salient object detection algorithm based on foreground seeds and center priori fusion is proposed. Firstly, using corner detection and edge linking alogrithms obtains two convex hulls, and the approximate position of the target region is preliminarily determined by their intersection points. Then using the convex hull edge as the standard, the similarity of the hyperpixel in the convex hull is detected, and removing superpixels similar to most external edges. Finally, a center priori model fused with the foreground seeds, and the final significant image is obtained by using Bayesian optimization framework. By comparing and merging different visual features, it is shown that this proposed algorithm is very effective in the saliency object detection.