Reviewerly/exHarmony huggingface dataset:
Upper Bounds and Algorithms for Construction of Deterministic Decision Trees for Decision Tables. Second Approach

In this chapter, upper bounds on the minimum complexity and algorithms for construction of deterministic decision trees for decision tables are considered. These bounds and algorithms are based on the use of so-called additive-bounded uncertainty measures for decision tables. The bounds are true for any complexity function having the property $$\varLambda 1$$. When developing algorithms, we assume that the complexity functions have properties $$\varLambda 1$$, $$\varLambda 2$$, and $$\varLambda 3$$.
Finding Public Domain and Creative Commons Images

Presentation slides for Finding Public Domain and Creative Commons Images by Corinne Kennedy. Presented at MegaResource 2020.
Simultaneous State and Parameter Estimation with Trajectory Shape Constraints (Poster)

In some tracking scenarios, the target state is subjected to equality constraints due to external limitations or inherent properties. If the constraints are known a priori, more accurate state estimates can be produced by taking advantage of these additional information in tracking algorithms. In this paper, a new model of the trajectory shape constraint is proposed when the target trajectory is known to be a straightline. The unknown slope and intercept of the straightline are treated as states to be estimated along with the target state. Then, two pseudo-measurements are constructed and augmented into the measurement equation in the filtering process. A trajectory shape constraint augmented state filter (TSC-ASF) is developed to produce constrained state estimates and constraint parameter estimates simultaneously. The nonlinear radar measurements and pseudo-measurements are processed by the converted measurement Kalman filter (CMKF) and unscented Kalman filter (UKF), sequentially. The unscented transform (UT) is employed to initialize the filter. Monte-Carlo simulation results illustrate the effectiveness of the proposed algorithm.
A Survey on Anomalous Topic Discovery in High Dimensional Data

Generally, a finding of a surprising data i.e. anomalies from discrete data leads towards the better cognizance of atypical direct of examples and to perceive the base of anomalies. Anomalies can be portrayed as the examples that don't have standard lead. It is similarly called as anomaly detection. Anomaly detection systems are generally used for deception detection in control cards, bank coercion; sort out interference et cetera. It tends to be escaped as, peculiarities, deviation, extraordinary cases or special case. Such kind of examples can't be seen to the symptomatic significance of a special case, as extraordinary inquiry till it has been joined honestly. A bundle examination procedure is used to perceive little scale clusters formed by these anomalies. In this paper, we demonstrate distinctive methods existed for perceiving anomalies from datasets which just recognizes the individual anomalies. Issue with particular anomaly detection technique that distinguishes anomalies using the entire features regularly disregard to recognize such anomalies. A technique to perceive cluster of bizarre data join indicate atypical territory of a little subset of features. This strategy uses an invalid model to for ordinary topic and after that distinctive test to distinguish all clusters of bizarre examples.
Change Extraction in Video Using Image Processing

In our project we are going to implement an effective scene change detection method utilizing a solo calculation and theprocedure of item following dependent on the aftereffects of the division. By utilizing this strategy we will perform not justaccurate scene change detection, but also obtain object level information of the video outlines, which is exceptionally valuablefor video content ordering and examination. We will distinguish moving articles in shading picture successions which are getfrom web camera or any video creating gadget. A video stream is prepared at different levels by contrasting the present videooutline and the past edge. We are going to utilize calculation in which we firstly separate the video into outlines. At that pointwe will grayscale those edges and afterward make a mass. After that we join those masses.We are goingtostorethoseimagesobtainedfromthevariousvideosintosamelocationand then we will gather the pictures of a videoto make same video again by applying the concept of data mining and indexing. Then we will try to calculate the differencebetween the edges with the assistance of those made masses
METHODOLOGY OF UNIVERSITY DEVELOPMENT THROUGH THEORETICAL AND ANALYTICAL CONCEPTS OF SMART UNIVERSITIES

This research is interested in studying the impact of the technologies on the universities theoretical concepts, and the emergence of modern concepts through the modern technologies including virtual, electronic, and intelligent universities. The study found that initiating the role of these modern concepts is the direction that city planners and designers should take when developing universities. This is through studying the theoretical and analytical concepts of some models to explore the components of development in an attempt to develop a design and development methodology through a set of concluded components. This aims to raise generations that are capable of contributing to build knowledgeable society and be integrated within the digital transformation, currently aspired by countries. Also, encouraging universities to build smart electronic systems and services that improve the quality of higher education services.
Pengembangan Aplikasi Perencana Wisata “Plesir” Berbasis Web

Indonesia is a country that has extraordinary natural wealth, both from the sea and the land. Not only natural tourism, art tourism, even culinary tourism is no less interesting. The many of tourist objects makes tourists confused to choose which tourist objects to visit and indeed according to their interests, given the limited time to travel. This time limitation makes it difficult for tourists to schedule their tour plans, such as how many tourist attractions can he actually visit with the tour time he has. Moreover, to choose a tourist attraction that suits the interests of each tourist, information about the tourist attraction itself is needed. The limitations of promotional media that can be used by UMKM create many limitations between UMKM and tourists. For this reason, it is necessary to develop a Plesir Tourism Planning Application Based on Web using the RUP (Rational Unified Process) methodology. While the steps taken are inception, elaboration, construction and transition. The results of developing this application are making it easier for tourists to use tourism, can get more information about tourist attractions and help UMKM in promoting their products.
An Approach to Automatically Detect and Visualize Bias in Data Analytics.

This work has been co-funded by the ECLIPSE-UA (RTI2018-094283-B-C32) project funded by Spanish Ministry of Science, Innovation, and Universities. Ana Lavalle holds an Industrial PhD Grant (I-PI 03-18) co-funded by the University of Alicante and the Lucentia Lab Spin-off Company.
An Ecological Approach to Technology Appropriation

The study aims to construct an innovation-based digital learning material (DLM) development ecosystem that penetrates and sustains within the school culture in order to diffuse and maintain DLM development. Sixty-two teachers from 21 different branches participated in this nested mixed design study. Data were collected through scales and interviews, and the study was based on the steps adopted by Rogers on the diffusion of innovation. Quantitative data were analysed with cluster analysis, logistic and multi-linear regression. Qualitative data were analysed with structural, in vivo, and axial coding in order to construct the ecosystem. The findings demonstrate that the main variables, which were determined to influence the adoption and sustenance of DLM development by teachers, were the support of administrators and colleagues, the willingness and need to develop DLM, computer knowledge, and DLM development self-efficacy.
Peningkatan Teknologi Berbasis Komputer bagi Remaja dan Pemuda Warga Gampong Jambo Timu Kecamatan Blang Mangat Kota Lhokseumawe

Improvement of computer-based technology for teenagers and young people of Jambo Timu Village, Blang Mangat Subdistrict, Lhokseumawe City, which is planned to be held in the Accounting Laboratory of the Department of Commerce of the Lhokseumawe State Polytechnic. Jambo Timu Gampong is one of the villages in Blang Mangat Subdistrict which has an area of approximately 92.5 Ha with a population of 1119 people with a total of 289 households. This is a target area for Lhokseumawe State Polytechnic. This technology improvement activity provides participants with knowledge and understanding of the basic concepts of computer applications, especially Microsoft Word and Excel. This activity was attended by 8 participants from the village of Jambo Timu, with the target of being expected to be able to understand and apply Microsoft Word and Excel in their work activities. In addition, participants also had one expertise and gave confidence to the participants in facing workplace competition. After conducting the training for 2 days, the trainees were able to understand and practice the Microsoft Word and Excel applications well, in general the success of this training achieved results of 87.5%
An Measurement Method of Ancient Poetry Difficulty for Adaptive Testing

As traditional Chinese culture education grows fast, adaptive testing for ancient poetry seems promising. The prerequisite of adaptive learning is question bank, while the quality of question bank depends on the rationality of question difficulty. The paper proposes a method that measures ancient poetry difficulty for objective questions and builds a measurement system. The method includes five steps: (1) Identify the verses corresponding to the question. (2) Get four indexes of the corresponding verses: search volume, correctly answered frequency, sentence length and grade of the textbook which includes the verses.(3) Use analytic hierarchy process to index system for weight assignment. (4) Compute the weighted sum of the four indexes as the measurement of difficulty. (5) Classify the question bank according to the calculated difficulty. Experimental results showed the effectiveness of this measurement method, which thereby can be used in various adaptive testing of ancient poetry.
Analisis Penerimaan Teknologi (Studi Kasus: Sistem Informasi Akademik STIE Lembah Dempo Pagaralam

The development of information technology has changed the world to be completely easy, and thanks to the support of computer technology it is proven that the mechanism of long and repetitive work becomes effective and efficient. This study aims to see the acceptance of technology (Academic Information System) in STIE Lembah dempo by using a research model developed by Venkatesh (2003) by ignoring moderator variables. The data in this study were obtained from a questionnaire with a total sample of 230 with freedom of 0.05%, respondents in this study were STIE students. From the results of table 4.14 by looking at the comparison of T-Table and T-Statistic shows that the value of T-Statistics &gt; T-Table so that it can be said that all variables have a positive effect on the acceptance of Academic Information Systems at the high school.
ANALISIS BIAYA MUTU DALAM MENINGKATKAN DAYA SAING PENDIDIKAN

Quality education is an expensive investment. Public awareness to bear the cost of education in essence will give a power to the community to be responsible for the delivery of education. Quality costs are costs that if there were no such costs, then an educational institution that we live will continue to run but will not be in accordance with what we expect or are expected by the community. The 1945 Constitution and Law No.20 of 2003 concerning the National Education System are legally binding, the Indonesian State mandates the government to set an education budget of 20 percent of the State budget as stipulated in article 31 paragraph 4. The purpose of this study is to identify the factors that affect the cost of education, types of education costs and models of education financing that are effective and efficient. This study was conducted by conducting a descriptive analysis of documents relating to education costs. The results of the study indicate that factors affecting the cost of education include: a) increases in prices b) changes in teacher salaries c) changes in population and increases in the percentage of children in public schools d) increases in education standards e) increases in guidance for higher education. Types of education costs include: a) direct costs b) indirect costs c) personal costs d) community costs, an effective and efficient educational funding model that is a human capital model where aspects of financing can affect the level of productivity that can affect the level of income of a person or group so can ultimately contribute to the speed of economic growth and development. Keywords : Quality Costs, Educational Institutions, competitiveness
Shoot-Out: Exploring HyperNEAT for an optimal Final-Third Approach in Robocup-2D Soccer

Past research have propositioned HyperNEAT to be suitable for domains that express spatial regularities. The regularities facilitate an indirect encoding scheme that expresses the topology of the decision-making Artificial Neural Network (ANN). The Robocup-2D soccer sub-task of Keepaway is one such domain, where HyperNEAT has shown not only to be effective at solving Keepaway, but also task transferable by scale using back the same CPPN that encodes the ANN topology. In this paper, we explore whether HyperNEAT could not only train a team of keepers to perform Keepaway at the opponents' half, but also for each keeper to consider shooting towards a well-guarded goal with an opposing taker scripted as the goalkeeper. In short, the expanded task is termed `Shoot-Out'. The paper presents the extensions to Verbancsis's HyperNEAT-BEV framework for Shoot-Out, and a discussion of the results obtained against runs from a policy where keepers take random actions (Random), and also against a policy of keepers with scripted behaviors (Hand-Coded).
Development Analysis of Government E-Government System Under the Background of Cloud Computing

With the rapid development of Internet and computer technology, cloud computing, as a computing method under big data, is becoming a research hotspot at home and abroad. Cloud computing is carried out on the basis of the Internet platform, using the virtualization technology that has been relatively mature at present. It can manage and dispatch a large number of resources uniformly through the network, so as to provide better services for users. Especially in the rapid development of mobile communication technology, cloud computing has been widely used in the field of government administration. For government administrative work, some of the original systems mainly use SMS, MMS or some simple client to work, slightly more complex work is difficult to complete. In this case, the original simple system cannot have big data interaction under the background of modern office, nor can it bring good interaction perception, so the existing government office system needs to be upgraded. The e-government system based on cloud computing is a good attempt.
The Onset of Connectivity

Placing identical finite sized objects randomly in a plane leads first to cluster formation, eventually to the formation of overall connection: this process is denoted as percolation. It is a geometric form of critical behavior, occurring with an abrupt onset. Such phenomena form the basis of many transitions, from Galaxy formation to the onset of electrical conductivity. The formation of locust swarms can be considered as such a process.
A Systematic Literature Review of Data Classification Techniques

The data mining and their different applications are becomes more popular now in these days a number of large and small scale applications are developed with the help of data mining techniques i.e. predictors, regulators, weather forecasting systems and business intelligence.Many of classification algorithms are available to analyze data.Classification is used to classify each item in a data set into one of a predefined set of classes or groups.Classification is the chore of identifying a model or function.There are two kinds of model are available for namely supervised and unsupervised.The performance and accuracy of the supervised data mining techniques are higher as compared to unsupervised techniques therefore in sensitive applications the supervised techniques are used for prediction and classification.In this presented work the supervised learning based data mining techniques for classification and prediction are analyzed.
<i>Enforcement of Intellectual Property Rights in the EU Member States</i> Flip Petillion (ed.)

Enforcement of Intellectual Property Rights in the EU Member States Filip Petillion (ed.) Enforcement of Intellectual Property Rights in the EU Member States Filip Petillion (ed.) Intersentia, 2019 ISBN: 978-1-78068-681-3, Hard cover, pp. xxviii + 1244 Price: €205 Marius Schneider Marius Schneider Marius Schneider, Consulting editor of JIPLP, Attorney-at-law at the Brussels bar and Legal Adviser in Mauritius. Email: ms@cew-law.be Search for other works by this author on: Oxford Academic Google Scholar Journal of Intellectual Property Law & Practice, Volume 15, Issue 4, April 2020, Page 308, https://doi.org/10.1093/jiplp/jpaa026 Published: 10 March 2020 Article history Received: 12 February 2020 Accepted: 14 February 2020 Published: 10 March 2020
Financial Network Representation Learning for Time Series Investment Behavior

Stock market is one of the important parts of modern financial networks. The mass data of stock transaction contain valuable but sparsely distributing information. How to implement effective data analysis and excavate commercial value on financial data have been appealing to the whole industry and academia. The traditional research on stock network mainly starts from the correlation of stock prices to build a trading network and conduct research, ignoring the role and value of equity investment information. Financial network representation learning for time series investment behavior, combined with the special nature of financial transactions in the stock market on the basis of the traditional network learning based on random walk, a high efficiency financial network random walk model is proposed, mapping the timing concept into the node interaction tour mechanism while taking into account the investment behavior, from which the embedded vector is more able to support downstream machine learning tasks such as funding community discovery.
Automatic document clustering and indexing of multiple documents using KNMF for feature extraction through Hadoop and lucene on big data

The existence of unlabeledtext data in documents has become larger and excavating such datasets is a provocative task. The objective of Big Data is to store, retrieve and analyse multipletext documents. Problem Statement:The retrieval of the identical data over large databases is of major concern. Existing Solution:Existing problem is solved by Full-Text Search (FTS) which means pattern matching technique that allows searching of multiple keywords at specific time.Proposed Solution:  In this paper, we consider multiple text documents as input and processed using text mining pre-processing algorithms like Key Phrase extraction, Porters stemming for tokenizing and TF_IDF toobtain all non-negative values. These values further processed to get matrix data throughNonnegative matrix factorization (NMF). On performing NMF, K-means algorithmis upgraded with NMF to obtain quality clusters of data sets.Performances of the algorithms are tested using Newsgroup20 data in Open Source Hadoop software environment which also analyses the performance of the MapReduce framework. The final outcome is to generate clusters and index them for the Newsgroup20dataset. Later on, Apache Lucene is presented for automatic document clustering with aGUI interface developed for indexing. Thus, this proposed algorithm resultsby improving the performance of document clustering through Map Reduce framework in Hadoop.
Image captioning with SEBL net: Squeeze and excitation block combined with Bi-long-short term memory network

Rapid developments in the advancing Deep Learning (DL) made significant progress in the methodologies for Automated Captioning. Automatic captioning for digital images or videos is a great challenge in Artificial intelligence. Though most algorithms used Convolution Neural networks (CNN), this work emphasize the use of Squeeze and Excitation (SE) technique with the Long Short- Term Memory (LSTM). This combination works well to generate the caption from a sequence of words based on the learning. This proposed work bridges the gap between visual and language system by combining the two vital methodologies for image caption.Keywords: Deep Learning, Image Captioning, Long-Short Term Memory Block, Squeeze and Excitation, Convolution Process
LibGuides: Libraries Resources and Services During COVID-19 Crisis: Free Publisher Resources During Coronavirus Outbreak

Relevant information related to ODU Libraries operations, resources and services during the COVID-19 crisis.
Privileged Pooling: Supervised attention-based pooling for compensating dataset bias

In this paper we propose a novel supervised image classification method that overcomes dataset bias and scarcity of training data using privileged information in the form of keypoints annotations. Our main motivation is recognition of animal species for ecological applications like biodiversity modelling, which can be challenging due to long-tailed species distributions due to rare species, and strong dataset biases in repetitive scenes such as in camera traps. To counteract these challenges, we propose a weakly-supervised visual attention mechanism that has access to keypoints highlighting the most important object parts. This privileged information, implemented via a novel privileged pooling operation, is only accessible during training and helps the model to focus on the regions that are most discriminative. We show that the proposed approach uses more efficiently small training datasets, generalizes better and outperforms competing methods in challenging training conditions.
An Efficient Image Data Encryption Technique Based on RC4 and Blowfish Algorithm with Random Data Shuffling

In this work, an able safety structure has been proposed which is efficient in image data security as well as the image loss is minimum. In our approach, we have used the combination of RC4 and Blowfish algorithms. Blowfish is suitable for image data for which the key does not vary for a single cycle. It is not suitable where more compact ciphers are needed. Due to the advantages of RC4 substitution in different rounds and Blowfish algorithm for the image data, we have used the combination for image data security. Bit shuffling has been performed for the bit randomization and XOR operations, so that the proper bit shuffling is possible. Then, for comparative analysis, peak signal-to-noise ratio (PSNR) and mean square error (MSE) cover have been designed. It provides the comparative and analytical comparison based on the different images. Our results show that it is efficient in terms of information loss, MSE, and PSNR values.