Reviewerly/exHarmony huggingface dataset:
Digital Rights Management Platform Based on Blockchain Technology

Traditional copyright protection technologies are unable to adapt to the current digital era of the Internet, and some problems such as difficult in digital works confirmation copyright, complex copyright transactions, copyright protection and infringement monitoring. Although Digital Rights Management (DRM) can provide certain security for digital copyrights, it still cannot effectively solve these problems. The decentralization, non-tampering, traceability and other characteristics of the blockchain are of great help to address the above problems. This paper proposes a scheme combining blockchain technology with digital rights management, and builds a digital rights management platform based on blockchain. In this paper, we propose platform system architecture based on blockchain technology by using the open source Hyperledger Fabric framework as the underlying blockchain development platform and combining smart contracts, IPFS systems, timestamps and other technologies, and have implemented the functions of the four core modules of the platform, which brings new breakthroughs for digital copyright protection technology.
Higher-Order Quantum Genetic Algorithm and Its Application on Non-Linear Equations

Abstract The high efficiency of quantum algorithms is caused by the quantum parallelism of the superposition principle and the quantum entangled, but traditional quantum genetic algorithms only use the quantum superposition principle. In order to further improve the performance of the algorithm, this paper proposes a new higher-order quantum genetic algorithm, which adds quantum entanglement properties on the basis of the principle of quantum superposition. Finally, the traditional quantum genetic algorithm and the higher-order quantum genetic algorithm are used to test a set of nonlinear equations many times. The results show that compared with the traditional quantum genetic algorithm, the evolutionary update operation of the higher-order quantum genetic algorithm does not involve multiple judgment conditions of the traditional quantum gate, requires less evolutionary algebra, and can converge quickly.
Design and Analysis of an Efficient Multi-Relational Decision Tree Learning Algorithm

Abstract In today’s day of modern era when the data handling objectives are getting bigger and bigger with respect to volume, learning and inferring knowledge from complex data becomes the utmost problem. The research in Knowledge Discovery in Databases has been primarily directed to attribute-value learning in which one is described through a fixed set tuple given with their values. Database or dataset is seen in the form of table relation in which every row corresponds to an instance and column represents an attribute respectively. In this paper a New framework is introduced a much more sophisticated and deserving approach i.e., Hybrid Multi-Relational Decision Tree Learning Algorithm which overcomes with Exiting technology drawbacks and other anomalies. Result show that Hybrid Multi- Relational Decision Tree Learning Algorithm provides certain methods which reduces its execution time. Experimental results on different datasets provide a clear indication that Hybrid Multi-Relational Decision Tree Learning Algorithm is comprehensively a better approach.
A Fuzzy Logic Approach for Improved Simulation and Control Washing Machine System Variables

To automate the washing process, determination and measurement of system variables is a challenging task. This paper describes the arrangement of different sensors used to detect the system variables as dirtiness of laundry, the quantity of laundry, humidity, and level inside the drum. Accordingly, the fuzzy controller automatically selects the different controlling variables as washing time, amount of detergent, water level, and drying time. The main contribution of this paper is to present the simulation of system variables obtained through various sensors incorporated in the washing machine design using MATLAB fuzzy logic toolbox.
Velocity in Software Engineering

Software engineering occupies an increasingly critical role in companies across all sectors, but too many software initiatives end up both off target and over budget. A surer path is optimized for speed, open to experimentation and learning, agile, and subject to regular course correcting. Good ideas tend to be abundant, though execution at high velocity is elusive. The good news is that velocity is controllable; companies can invest systematically to increase it.
The Family of Boolean Approaches

This chapter proposes a brief introduction to the BooleanBoolean approach family family approaches and it gives an overview of what is developed in Part 3 of the book.
Introduction of the Basic Human Resource Development Program for Freshman and the Practical Professional Development Program for Current Students at Tokyo University of Agriculture and Technology

The Internet and information systems have been used widely as part of the social infrastructure. With the commoditization of these technologies, the importance of human resources to handle them is increasing day by day. However, there are not enough infrastructure engineers to handle information infrastructure, so the development of these professionals is an urgent necessity. Universities also have a variety of information systems, and by effectively utilizing these systems, advanced research and education can be carried out. The Information Center is an organization that promotes the operation and utilization of these information systems. The Tokyo University of Agriculture and Technology Information Media Center (IMC) operates the university’s information infrastructure, and in conjunction with this, it also provides education on the effective use of information infrastructure. First of all, there is an information orientation as a basic education program for freshman. This program is conducted at the beginning of the new semester to educate students on the use of the university’s information system and information ethics. It is also considered that the role of a science and technology university is to develop engineers who support information systems as a social infrastructure. Therefore, IMC provides an educational program for the development of infrastructure engineers, which is separate from the regular curriculum of our university. In this program seminar-style classes are held with exercises covering a wide range of content in keywords such as Internet technology, cloud computing, server operation technology, and security. In addition, this program is based on a program that has been developed for working people outside the university and is being implemented as the original program for the university with advanced content. Our students belong to their own specialties, such as engineering and agriculture, but by providing other programs for these students we can develop a wide variety of human resources by acquiring knowledge of information technology, not only in their own specialties but also in a wide range of fields. This paper outlines these two programs that IMC provides as programs mainly for students. This paper will help to consider the human resource development programs that can be implemented in universities, from basic programs to practical and advanced programs.
3 Dimensional Visualization for Adaptive Building Construction Masterplan in University of Darussalam Gontor Area

Abstract University of Darussalam Gontor (UNIDA Gontor) is one of the universities that has been developing quite significantly in Ponorogo area since 2014, especially in terms of facilities and infrastructure. Until now, UNIDA Gontor has a development master plan in physical form in the form of paper. However, there are several drawbacks to the physical prototype. First, physical documents are vulnerable to damage and loss. Second, with the condition of the campus that continues to develop, it is difficult to update the prototype in physical form. Third, physical documents cannot provide a visual representation of the buildings that have been and will be built. To overcome some of these weaknesses, it is necessary to have a solution with technology that is adaptive to needs. The purpose of this research is the design of 3-dimensional modeling of buildings in UNIDA Gontor area that can be accessed easily and dynamically. The project design is based on the steps of the waterfall model with the following stages: (1) Requirements, (2) Design (3) Implementation, (4) Verification, and (5) Maintenance. Testing is done by testing a 3-dimensional display with a 3D image viewer which shows a success rate of 90% working as expected. The test is also carried out by measuring the level of color suitability, layout and comparison of the size of the 3D media with the actual building size, implemented through distributing questionnaires to relevant parties at the university and the public gets 88% according to their wishes. The resulting media is a adaptive building asset based on 3D modeling that can be accessed easily anytime and anywhere. With various existing features, it is hoped that the resulting media can facilitate access for the community and the development activities carried out at UNIDA Gontor. The long-term benefits of this research are taken into account in the framework of the university’s development plan and its infrastructure.
A new heuristic algorithm for fast k-segmentation

The k-segmentation of a time-indexed data stream is used to partition it into k piecewise-linear segments, so that each linear segment has a meaningful interpretation. Such segmentation may be used to summarize large datasets with small sub-samples, to identify anomalies within segments, detect change points between segments, and to select critical subsets for training machine learning models. Exact and approximate segmentation methods for k-segmentation exist in the literature. Each of these algorithms provides a different trade-off between computational complexity and accuracy. A novel heuristic algorithm is proposed in this paper which provides accuracies competitive with exact methods at a fraction of the computational expense.The new algorithm is inspired by Lloyd's algorithm for K-Means and Lloyd-Max algorithm for scalar quantization, and is called the LM algorithm for convenience. This algorithm iteratively minimizes a cost function from any given initialisation of partitions; the commonly used L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> cost is chosen in this paper. The greedy nature of cost minimization makes the algorithm sensitive to the choice of initialisation. However, the same sensitivity allows convergence to different local optima when starting with different initialisations. Three variants of the proposed algorithm are tested over a large number of synthetic datasets, one being a standalone LM implementation, and two others that combine with existing algorithms. One of the latter two - LM-enhanced-Bottom-Up segmentation - is found to have the best accuracy and the lowest computational complexity among all algorithms. This variant of LM can provide k-segmentations over data sets with up to a million high-dimensional data points within several seconds.
Effect of Various Factors in Context of Feature Selection on Opinion Spam Detection

With the growing popularity of online reviews, spammers often target specific products or services with the aim to mislead consumers in their purchase decisions. This has opened doors for researchers to study the problem of opinion spam detection. Till date, many effective and efficient solutions have been proposed in this regard using various types of features. However, most of the feature engineering tasks extract thousands of features, which may lead to degrade the performance and increase computation cost involved in many machine learning algorithms. Feature selection methods can greatly improve classification performance along with the reduction in computation cost of model training. In this paper, we investigate the effect of different feature selection techniques on opinion spam detection. For the same, various feature selection methods (filter-based and model-based) with varying number of features have been employed to train four different classification models. In addition, three well-known review datasets from different domains (hotel, doctor and restaurant) and four different types of features, viz., unigram, bigram, part- of-speech frequency count and word embedding, have been used to examine the impact of different factors responsible to improve the performance in opinion spam domain. Our experimental results demonstrate how different factors affect classification performance and cost, which is statistically validated by using Analysis of Variance test.
Reducing the Number of Queries in the Search Text Fuzzy Duplicates

The article considers problems of text fuzzy duplicates detecting. The work is devoted to the problem of reducing the queries number to Internet search systems when searching for duplicates on the Internet. Dividing the source document into parts and generating queries for each of them can significantly reduce the load on search engines and increase the speed of finding duplicates. The most effective way to generate requests is to identify key sentences. The sentences with the highest sum of word weights calculated taking into account the global word frequency are selected as key sentences. The paper presents the results of analyzing the effectiveness of text selection for queries in various ways. Part of text starting with key sentence (complete sentences). The best results are obtained by selecting a part of the text starting with a key sentence, up to 400 characters long as the query text. The approaches described in article to searching duplicate texts using key sentences used in plagiarism search system Author.NET.
Redactable Signature with Compactness from Set-Commitment

Redactable signature allows anyone to remove parts of a signed message without invalidating the signature. The need to prove the validity of digital documents issued by governments is increasing. When governments disclose documents, they must remove private information concerning individuals. Redactable signature is useful for such a situation. However, in most redactable signature schemes, to remove parts of the signed message, we need pieces of information for each part we want to remove. If a signed message consists of l elements, the number of elements in an original signature is at least linear in l. As far as we know, in some redactable signature schemes, the number of elements in an original signature is constant, regardless of the number of elements in a message to be signed. However, these constructions have drawbacks in that the use of the random oracle model or generic group model. In this paper, we construct an efficient redactable signature to overcome these drawbacks. Our redactable signature is obtained by combining set-commitment proposed in the recent work by Fuchsbauer et al. (JoC 2019) and digital signatures.
GAN Assisted Reduction of Misclassification of Noisy Images

One of the problems in the image classification is the misclassification when there are slight noises that are not visible to the human eyes. In this study, GAN-based machine learning is used to correctly classify the images with noises. VGG16 is adopted as the image classifier. By adding small noises to the images, we artificially create noise images that the classification model misclassifies. A noise reduction model based on GAN method is designed and the facility of the model is evaluated.
Real-Time Capable Sensor Data Analysis-Framework for Intelligent Assistance Systems

In the industrialized countries, the very old part of the population has been growing rapidly for many years. In the next few years in particular, the age cohort over 65 will increase significantly. This goes hand in hand with illnesses and other physical and cognitive limitations. In order to enable these people to remain in their own homes for as long as possible despite physical and cognitive restrictions, technologies are being used to create ambient assisted living applications. However, most of these systems are neither medically verified nor are latencies short enough, for example, to avoid falls. In order to overcome these problems, a promising approach is to use the new 5G network technology. Combined with a suitable sensor data analysis frame work, the fast care project showed that a real-time situation picture of the patient in the form of an Avatar could be generated. The sensor structure records the heart rate, the breathing rate, analyzes the gait and measures the temperature, the VOC content of the room air, and its humidity. An emergency button has also been integrated. In a laboratory demonstrator, it was shown that the infrastructure realizes a real-time visualization of the sensor data over a heterogeneous network.
Did You Just Assume My Vector? Detecting Gender Stereotypes in Word Embeddings

Recent studies found out that supervised machine learning models can capture prejudices and stereotypes from training data. Our study focuses on the detection of gender stereotypes in relation to word embeddings. We review prior work on the topic and propose a comparative study of existing methods of gender stereotype detection. We evaluate various word embeddings models with these methods and conclude that the amount of bias does not depend on the corpora size and training algorithm, and does not correlate with embeddings performance on the standard evaluation benchmarks.
A Comparison of Neural Networks Architectures for Diacritics Restoration

Neural networks are widely used for the task of diacritics restoration last years. Authors use different architectures of neural network for selected languages. In this paper, we demonstrated that an architecture should be selected according to a language in hand. It also depends on a task one states: low and full resourced languages could use different architectures. We demonstrated that common used accuracy metric should be changed in this task to precision and recall due to the heavy unbalanced nature of the input data. The paper contains results for seven languages: Croatian, Slovak, Romanian, French, German, Latvian, and Turkish.
Reading Habits of Young People in the Context of Digital Progress: An Example of Research of Republic Croatia

The aim of the research part of the thesis is to investigate the frequency of use of certain media, attitudes, and preferences of young people towards reading and to investigate whether the reading habits of young people change under the influence of digital media. The study was carried out on a sample of 132 students between the ages of 13 and 19 in the Virovitica-Podravina, Osijek-Baranja, Bjelovar-Bilogora, and Vukovar-Srijem counties. The respondents are elementary school students (7th and 8th grade) Classes and high school students (high schools and vocational schools). To conduct the research, and an online questionnaire was created to collect the necessary data. The results of the study showed statistically significant differences in reading in relation to gender, but also in reading books in a digital edition in relation to the place of residence. The results of the study showed statistically significant differences in reading in relation to gender, but also in reading books in a digital edition in relation to the place of residence. The analysis of variance revealed a statistically significant difference in reading educational content in relation to the type of school. The results of this study show that more than half of students (54.5%) enjoy reading, but most of them (59.1%) still do not read in their spare time.
Nakamoto Consensus to Accelerate Supervised Classification Algorithms for Multiparty Computing

Bitcoin mining consumes tremendous amounts of electricity to solve the hash problem. At the same time, large-scale applications of artificial intelligence (AI) require efficient and secure computing. There are many computing devices in use, and the hardware resources are highly heterogeneous. This means a cooperation mechanism is needed to realize cooperation among computing devices, and a good calculation structure is required in the case of data dispersion. In this paper, we propose an architecture where devices (also called nodes) can reach a consensus on task results using off-chain smart contracts and private data. The proposed distributed computing architecture can accelerate computing-intensive and data-intensive supervised classification algorithms with limited resources. This architecture can significantly increase privacy protection and prevent leakage of distributed data. Our proposed architecture can support heterogeneous data, making computing on each device more efficient. We used mathematical formulas to prove the correctness and robustness of our system and deduced the condition to stop a given task. In the experiments, we transformed Bitcoin hash collision into distributed computing on several nodes and evaluated the training and prediction accuracy for handwritten digit images (MNIST). The experimental results demonstrate the effectiveness of the proposed method.
Cost-Sensitive Deep Learning with Layer-Wise Cost Estimation

While deep neural networks have succeeded in several applications, such as image classification, object detection, and speech recognition, by reaching very high classification accuracies, it is important to note that many real-world applications demand varying costs for different types of misclassification errors, thus requiring cost-sensitive classification algorithms. Current models of deep neural networks for cost-sensitive classification are restricted to some specific network structures and limited depth. In this paper, we propose a novel framework that can be applied to deep neural networks with any structure to facilitate their learning of meaningful representations for cost-sensitive classification problems. Furthermore, the framework allows end-to-end training of deeper networks directly. The framework is designed by augmenting auxiliary neurons to the output of each hidden layer for layer-wise cost estimation, and including the total estimation loss within the optimization objective. Experimental results on public benchmark data sets with two cost information settings demonstrate that the proposed framework outperforms state-of-the-art cost-sensitive deep learning models.