Reviewerly/exHarmony huggingface dataset:
The implementation of smart city through android-based device : ”Klaten Smart City Application”

Abstract Klaten is one of regions that elected in the movement towards 100 smart cities in Indonesia launched by The Ministry of Communication and Informatics. Due to this movement, an in-line application is created, particularly for Klaten regency which is named Klaten Smart City Application. Klaten Smart City Application is an Android-based device that can be downloaded via Google Play. This paper aims to identify the features of Klaten Smart City Application and to identify opinion of the Klaten government towards the development of Klaten Smart City Application. This research uses a qualitative method. The primary data was obtained from in-depth interview with representative of The Klaten Informatics Communication Department. The secondary data was obtained by direct exploring Klaten Smart City Application. Based on the result of this research, Klaten Smart City Application has 27 features. The opinion from Klaten Informatics Communication Department about Klaten Smart City Application is graphically perfect, it is a good application because it displays the mapping of several services in Klaten.
English Applied Ability Teaching Reform of Mechanical Design and Manufacturing in ESP Teaching Mode

Abstract ESP Teaching method is an approach that is significantly different from the conventional English Language Teaching method. ESP teaching is a market-driven, communicative English teaching model which is based on the overall principle of significantly improving learners’ professional English social skills, learner-centered, and meets the needs of society and individuals. In this study, researcher’s attempt is to integrate ESP teaching model into design and manufacturing English teaching. The study aims to investigate whether ESP teaching method assist learners in mechanical design and manufacturing master the English skills of effective communication in the professional field. The study discusses the teaching reform in the teaching materials, teaching content, teaching methods and teaching assessment for mechanical design and manufacturing English. Some ideas, such as improving teaching methods and students’ learning initiative, have been adopted in teaching practice and the teaching effect is very good.
Multilingual Verbalization and Summarization for Explainable Link Discovery

The number and size of datasets abiding by the Linked Data paradigm increase every day. Discovering links between these datasets is thus central to achieving the vision behind the Data Web. Declarative Link Discovery (LD) frameworks rely on complex Link Specification (LS) to express the conditions under which two resources should be linked. Understanding such LS is not a trivial task for non-expert users. Particularly when such users are interested in generating LS to match their needs. Even if the user applies a machine learning algorithm for the automatic generation of the required LS, the challenge of explaining the resultant LS persists. Hence, providing explainable LS is the key challenge to enable users who are unfamiliar with underlying LS technologies to use them effectively and efficiently. In this paper, we extend our previous work (Ahmed et al., 2019) by proposing a generic multilingual approach that allows verbalization of LS in many languages, i.e., converts LS into understandable natural language text. In this work, we ported our LS verbalization framework into German and Spanish, in addition to English language. Our adequacy and fluency evaluations show that our approach can generate complete and easily understandable natural language descriptions even by lay users. Moreover, we devised an experimental neural approach for improving the quality of our generated texts. Our neural approach achieves promising results in terms of BLEU, METEOR and chrF++.
Comparative Evaluation of Face Detection Algorithms

Computer vision is shaping a new era with its constant development of SOTA algorithms. One heavily contested sub-field of computer vision is face detection, due to its versatile usage in many fields such as security, medical diagnosis, entertainment, and military applications. As the technology develops, it aims to run faster and more accurately on mobile devices and remote computers. In this paper, we aim to compare a number of the best face recognition algorithms and analyze the performance of each of them by deploying each algorithm on a Jetson Nano Developer Kit. Among the 6 algorithms discussed, Haar_FA1 showed the best performance in terms of precision. On the other hand, OpenCV_dnn had the best results in terms of recall. Lastly, in terms of execution time -as it is an important metric- face detectors that belong to the Haar classifier family dominated the comparison.
Bitcoin economic behavior analysis and policy implications by leveraging deep learning and high-frequency data

The recent surge in Bitcoin price performance has attracted significant attention from both the market and academic researchers.This paper constitutes the first principled attempt to determine market risk own-funds requirements for Bitcoin.To this end, we examine price microstructure of the USD per bitcoin, and compare to other financial variables, as a proxy toward classifying Bitcoin into the appropriate risk-class.Using the outcomes of this analysis, we classify and quantify the entailed risk from a market risk minimum capital requirements perspective.To perform the prescribed analysis, we introduce a novel methodological paradigm, which adopts bleeding-edge concepts from the field of Data Science and Machine Learning.
An Approach for e-Voting using Face and Fingerprint Verification

India is the world's largest democracy with nearly 900 million eligible voters. The election period in India spans over nearly six weeks for general elections and there is no alternative system working for eligible voters who are at outstations or willing to cast their vote but not able to do so due to location constraints. Moreover, there is no special provision made for NRI voter / overseas elector for whom it is difficult to vote in person at the polling station. Service voters have to use postal ballot and go through a tedious process to cast their vote and this process is also prone to human errors. Our system is an e-Voting system that will use fingerprint and face verification along with a combination of firebase-database and server-side file-system at its back-end. Our system is designed especially for NRI voters and Service voters for whom it is difficult to cast their votes through the existing system. It provides an efficient, convenient, and secure mechanism for voters to cast their votes. The design of this system will make the voting process more convenient and may, therefore, lead to improving the turnout.
Classification of Arabic Documents depending on Maximal Frequent Itemsets

Abstract In this paper we introduced techniques for classifying Arabic documents depending on association rules built from maximal frequent itemsets. Parallel Maximal Itemset Miner Algorithm (PMIMA) adopted several conditions to prune search space parallelly introduced for extracting maximal frequent itemsets. Rule length, rule weight and rule majority are three classification methods exploited to classification Arabic documents. Comparing with classification results obtained depending on all frequent itemsets extracted by Apriori, we proved efficiency of ours approach.
Evaluation of NDVI and NDWI parameters in CPU-GPU Heterogeneous Platforms based CUDA

Artificial intelligence is a field in full development, from facial recognition to autonomous vehicles and referral systems for online shopping, passing by smart farming, these new technologies are invading our daily lives.Nowadays, agricultural applications require more and more computer vision technologies for continuous monitoring and analysis of crop health and yield. That is why machine learning has become one of the mechanisms that make farming more efficient by using high-precision algorithms. This article deals with the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI), which are the most widely used indices in precision agriculture. In this work, we adopt GPU-based heterogeneous architecture using parallel programming with the CUDA language. The algorithm is evaluated on several platforms: NVIDIA Jetson TX1, DELL-desktop, and XU4 board. It has been discovered that the execution time of the two NDVI and NDWI indices on the embedded TX1 card is more optimized and improved compared to the execution time on the XU4 card and the Desktop.
Improved Similar Images Retrieval: Dynamic Multi-feature of Fusion a Method with Texture Features

Rapid advances in digital image technology contributes huge amount of images in the many fields like digital libraries, medical imaging, Web image searching, etc. This chapter attempts to present novel approach in CBIR, which is based on the combined texture features. This approach mainly aims at designing the effectiveness of the image retrieval system. Thus, in this chapter, multiple features method has been proposed in order to create a novel hybrid features by 4Dir-GLCMEDGE method. This method majorly has three process flows, (a) a process to find the four diagonal texture features (b) a process to find the local binary pattern (LBP) features (c) It is a fusion of diagonal texture features and edge features. This fusion aims at combining and reducing multiple image features into single image feature. Selecting a query image as input, more similar images can be retrieved with the help of the proposed hybrid features. Using the hybrid features, based on two distance metrics such as euclidean and manhattan metics, results in more similar imges. This proposed method has been tested with Corel and Wang datasets. The test results, compared to the previous methods, are encouraging and found to be better than existing methods. The image retrieval result improvements in the new approach method over existing methods have been proved by in terms of precision and recall.
A Combination of Lexicon-Based and Classified-Based methods for Sentiment Classification based on Bert

Abstract Sentiment classification is a crucial problem in natural language processing and is essential to understand user opinions. There are two main approaches to solve this problem, one is the classified-based method, the other is the lexicon-based method; however, both methods perform not well on the long-sequence methods, and each method has its advantages and disadvantages. This paper introduced a new method called Lexiconed BERT, which cream off the best and filter out the impurities from the above two methods. The evaluation shows that our model achieves excellent results in the long sequence sentence and reduce resource consumption significantly.
Linguistic Features to Consider When Applying Persona of the Real Person to the Text-based Agent

As artificial intelligence (AI) technologies advance, the possibility of developing virtual agents capable of mimicking human beings is increasing. Furthermore, AI techniques applicable to mimicking certain features of a specific person (e.g., facial expression, voice, motion) are becoming more sophisticated. Although the HCI community has explored how to design or develop AI agents mimicking a real person, limited studies on mimicking someone’s text-based behavior shown in the instant messaging exist. This study investigates the features that make users perceive text-based agents as people they know in reality. On top of the previous efforts of designing human-like virtual agents, our work suggests design guidelines for applying the persona of the real person (PRP) to text-based agents.
Improving biomedical word representation with locally linear embedding

Distributed word representation, usually obtained through calculation from large corpora, has been widely used in biomedical text because of its effectiveness in representing word semantic information. High-quality and meaningful biomedical words enable doctors to obtain the gist of information and knowledge in a short time to make clinical decisions quickly. Currently, the distributed word representation ignores the influence of the word embedding geometric structure obtained through calculation on the word semantic information and cannot accurately represent the word information, thus affecting the representation effect of biomedical text. To solve the above problems, we propose a biomedical word embedding framework based on manifold learning. Our work provides new perspectives for representing biomedical word embedding, which is the key concept in biomedical natural language processing tasks. First, the distributed word representation model is used to obtain the pretrained word embedding, and then the manifold learning is used to re-embed the pretrained word embedding. To verify the validity of the proposed framework in the biomedical domain, we evaluate the algorithm by using biomedical texts. Experimental results show that the proposed method can effectively improve the results of electronic health record classification and semantic similarity.
Model-Oriented Programming as a Consequence of the Structural Theory of Multi-Сomponent Complex Systems

The paper offers a new programming paradigm, which implements CAD methods in programming, The level of encapsulation in model-oriented programming is higher than in the object-oriented approach. The key features of the MO-programming are declarative style (no imperative programs), and focusing on the distributed and high-performance calculations. The method proposed is based on the structural theory of multi-component complex systems, and is applicable for a rather wide class of tasks including the elaboration of simulation models of such systems.
Transient electromagnetic inversion based on improved Quantum Genetic Algorithm

Due that Quantum-inspired Genetic Algorithm (QGA) converges slowly and falls into the local optimal solution easily in the inversion process, this paper applies an Improved Quantum-inspired Genetic Algorithm (IQGA) for the inversion calculation for the transient electromagnetic layered geoelectric model, and compared with that of QGA algorithm. The research shows that IQGA algorithm has better global optimization ability, faster convergence speed and has higher fitting degree for transient electromagnetic data inversion, which can be effectively applied in solving geophysical inverse problems.
Quantum Computing and Its Application in Healthcare and Agriculture

Quantum computing is one of the promising tools to address complex problems which take more time to provide solutions. Quantum computing works on the qubits or quantum bits. It has been found that the number of qubits is directly proportional to the performance of the system. Health and agriculture are the two important sectors which deal with different complex multi-objective and many-objective problems. As the objectives are associated with multiple features and multiple computations, quantum computing works on those multiple features at a time to address specific objectives within an optimal time period. This chapter explains the basics of quantum computing and application of it both in both the health and agriculture sectors.
CNN to Capsule Network Transformation

Capsule Network has been recently proposed which outperforms CNN in specific tasks. Due to the network architecture differences between Capsule Network and CNN, Capsule Network could not use transfer learning which is very frequently used in CNN. In this paper, we propose a transfer learning method which can easily transfer CNN to Capsule Network. We achieved by stacking pre-trained CNN and used the proposed capsule random transformer to interact individual CNN each other which will form a Capsule Network. We applied this method to U-net and achieved to create a capsule based method that has similar accuracy compared to U-net. We show the results on cell segmentation dataset. Our capsule network successfully archives higher accuracy compared to other Capsule Network based semantic segmentation methods.
WITHDRAWN: Improving electoral roll authenticity and accessibility using side-chaining and deep learning

The main issue for any organisational elections is the validity and accessibility of the electoral roll. The electoral roll requires data validation when data is added or updated, and requires effective data selection when data retrieval operations are required. It is an evolving process that needs continuous moderation from both the bureaucrats and the public. Because of their inherent properties, such as non-mutability, multi-level authentication, cryptography, and strict data update laws, Blockchain driven systems have proven to be efficient in terms of security. But due to large data chains, blockchain systems have limited retrieval performance, which slows down the read-cycles, rendering the system ineffective for big data applications. A side-chain-based solution for the efficient storage and recovery of electoral roll data is described in this paper. In comparison to single-chain systems, data updating and insertion into side chains is less reliable. This is due to the fact that as opposed to their single chain counterparts, side chains are very limited in scale, so targeting these side chains is simpler. In order to preserve the security of the side-chain scheme, this paper describes a deep learning cryptosystem (DLC). In order to create a special non-identifiable side chain structure, this DLC utilises a combination of adaptive cryptography and transfer learning. In order to ensure high fidelity and reduce the risk of attacks, this side-chain system uses a hybrid blockchain implementation. The proposed method, while retaining the same degree of accuracy and protection, is found to be 47 percent faster than the current single-chain electoral roll management solutions.
Additional professional education in a region as an indicator of its quality

The problem of transformation in the field of education and educational services is actualized by a wide range of concerned parties (specialists, parents, managers, government representatives). The innovative development vector has emerged also in the field of education through numerous upgrades and reforms (implemented and announced) focused on consideration of the specific socio-cultural features, such as sanitary and epidemiological situation, technologization, and digitalization of education, upgrading of the ethnic and cultural identity of regions in the context of growing migration flows and growth of the ethnic factor in the personality’s upbringing. The methodological background of the present research is based on the competence approach. The article presents an analysis of scientific research on the issue of pedagogical professional competence as the main resource that provides innovative processes and prospects for the development of additional education; highlights the essential characteristics of the concept of professional competence of a teacher, which are focused on the system of additional education through participation in project activities. The article substantiates the necessity of forming the professional competence of a teacher of additional education in innovative practices of contemporary technological society, modeling a new educational infrastructure capable of creating modern training resources in regional systems of additional education. The innovative experience of implementing the Federal project “Success of every child” of the national project “Education” aimed at creating new vacancies in the system of additional education for children of the Orel Region is considered for the first time. The authors contemplate the practices of additional education that are in demand among parents and students and are provided by the Quantorium Technopark in the city of Orel.
A Parallel Adaptive DBSCAN Algorithm Based on k-Dimensional Tree Partition

The existing parallel DBSCAN (density based spatial clustering of applications with noise) algorithm needs to determine the parameter settings manually, and the datasets will be repeatedly accessed in the process of data partitioning and data merging, which reduces the efficiency of the algorithm excuting. Therefore, this paper proposes a parallel adaptive DBSCAN algorithm based on k-dimensional tree partition. It divides the dataset into several balanced data partitions by using k-dimensional tree, and carries out parallel computing in spark distributed computing framework, thus increasing the concurrent processing ability of the algorithm program and improving the I/O access speed. In addition, the improved adaptive DBSCAN parameter method is applied to each data partition for clustering analysis to obtain local clusters, which solves the random problem of manual setting parameters in the clustering process, and ensures the data quality of clustering mining. At the same time of creating local clusters, this algorithm also puts the mapping relationship between data points and adjacent points into the HashMap data structure of the master node, and uses it to merge local clusters into whole clusters, which can reduce the time cost of data merging. The experimental results show that the proposed algorithm can save about 18% running time compared with RDD-DBSCAN algorithm without reducing the clustering quality. With the increase of the number of cluster nodes, the running efficiency of the algorithm can be further improved, so it is suitable for processing massive data clustering analysis.
BGP Neighbor Trust Establishment Mechanism Based on the Bargaining Game

The Border Gateway Protocol (BGP) is the standard inter-domain route protocol on the Internet. Autonomous System (AS) traffic is forwarded by the BGP neighbors. In the route selection, if there are malicious or inactive neighbors, it will affect the network’s performance or even cause the network to crash. Therefore, choosing trusted and safe neighbors is an essential part of BGP security research. In response to such a problem, in this paper we propose a BGP Neighbor Trust Establishment Mechanism based on the Bargaining Game (BNTE-BG). By combining service quality attributes such as bandwidth, packet loss rate, jitter, delay, and price with bargaining game theory, it allows the AS to select trusted neighbors which satisfy the Quality of Service independently. When the trusted neighbors are forwarding data, we draw on the gray correlation algorithm to calculate neighbors’ behavioral trust and detect malicious or inactive BGP neighbors.
Using CNN to Predict the Resolution Status of Bug Reports

Abstract Bug tracking systems (BTS) are a resource for receiving bug reports that help to improve software applications. They usually contain reports reported by the end-users or developers. Bug Reports contain some suggestions, complaints, etc. The problem is that every submitted bug report is not accepted to implement. Mostly bug reports are rejected because they are incomplete, duplicate, expired, etc. while only a few are accepted to implement. But the developers have to check every bug report manually that needs many resources (i.e. labour, money, time). In this study, we proposed a convolutional neural network (CNN) based approach to automatically classify bug reports as accepted and rejected. Results show that the proposed approach achieves the highest performance as compared to closely related works.